Generating consensus feeding appetite forecasts

ABSTRACT

Generating consensus feeding appetite forecasts include providing a first feeding parameter data set associated with a first feeding parameter to a first feeding appetite forecast model and providing a second feeding parameter data set associated with a second feeding parameter to a second feeding appetite forecast model different from the first forecast model. The first feeding appetite forecast model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second feeding appetite forecast model. An aggregated appetite score based on a combination of the first feeding appetite forecast model using the first weight factor and the second feeding appetite forecast model using the second weight factor. Further, a feeding instruction signal based at least in part on the aggregated appetite score is provided for modifying the operations of a feed control system.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority as a continuation of U.S. patentapplication Ser. No. 16/740,021 (Attorney Docket No. 1000-190003-US),entitled “METHODS FOR GENERATING CONSENSUS FEEDING APPETITE FORECASTS”and filed on Jan. 10, 2020, the entirety of which is incorporated byreference herein.

BACKGROUND

Husbandry, such as in agriculture and aquaculture, includes raisinganimals for their meat, fiber, milk, eggs, or other products. Animalfeed, such as fodder and forage, generally refers to food given toanimals. Fodder refers particularly to foods or forages given toanimals, rather than that which the animals forage for themselves.Fodder includes grains, silage, compressed and pelleted feeds, oils andmixed rations, and the like. Extensively reared animals may subsistentirely or substantially on forage, but more intensively reared animalswill typically require energy and protein-rich foods, such as providedby fodder, in addition to wild forage.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure may be better understood, and its numerousfeatures and advantages made apparent to those skilled in the art byreferencing the accompanying drawings. The use of the same referencesymbols in different drawings indicates similar or identical items.

FIG. 1 is a diagram illustrating a system for implementing feedingappetite forecasting in accordance with some embodiments.

FIG. 2 is a diagram illustrating a forecast system implementing two setsof different underwater sensors in accordance with some embodiments.

FIG. 3 is a diagram illustrating a first example of adaptive weightingof feeding appetite forecast models in accordance with some embodiments.

FIG. 4 is a flow diagram of a method for providing a feeding appetiteforecast in accordance with some embodiments.

FIG. 5 is a diagram illustrating a forecast system implementing a set ofunderwater sensors and a set of out of water sensors in accordance withsome embodiments.

FIG. 6 is a diagram illustrating a forecast system implementing two setsof image-based sensors in accordance with some embodiments.

FIG. 7 is a block diagram illustrating a system configured to provide afeeding appetite forecast in accordance with some embodiments.

FIG. 8 is a block diagram illustrating a second example adaptiveweighting of feeding appetite forecasts in accordance with someembodiments

DETAILED DESCRIPTION

In animal husbandry, farmers theoretically aim for the highest growthrate possible, using the least amount of feed to produce the bestquality output. Feed conversion ratio (FCR) is a ratio or rate measuringthe efficiency with which the bodies of animals convert animal feed intoa desired output. With dairy cows, for example, the desired output ismilk, whereas in animals raised for meat (e.g., beef cows, pigs,chickens, fish, shrimp, shellfish, and the like) the desired output isflesh/meat. In particular, the feed conversion ratio is the ratio ofinputs to outputs (e.g., mass of feed provided per body mass gained byan animal). In some industries, the efficiency of converting feed intothe desired output is represented as feed efficiency (FE), which is theoutput divided by the input (i.e., inverse of FCR).

Typically, aquaculture refers to the cultivation of fish, shellfish,aquatic plants, and the like through husbandry efforts for seafoodmarkets and human consumption. Intensive aquaculture relies ontechnology to raise fish and shellfish at higher densities.Aqua-culturalists generally attempt to increase control over factorssuch as water quality, temperature levels, oxygen levels, stockingdensities, and feed to promote growth, reduce stress, control disease,and reduce mortality; due to increased control over one or more of suchfactors, intensive aquaculture generally produces higher yields relativeto extensive aquaculture for the same species.

Fish are usually fed a diet of pelleted food, grains, and the like toincrease the mass of the resulting farmed fish. Feeding management isone of the more important aspects of aquaculture, as feeding costscontribute 30% or more as a proportion of total production expenses.Feeding management efficiency regarding feeding rate, amount, frequency,and timing should be adjusted to avoid both over- and under-feeding.Underfeeding, in particular, can result in a detrimental effect to thegrowth of fish due to reduced growth rates. Overfeeding can also resultin detrimental effects to the growth of fish due to overload of stomachand intestines which leads to a decrease in digestive efficiency andfeed utilization. Further, overfeeding can also result in reduction ofprofitability due to waste of feed (e.g., particularly with open- orsemi-open systems in aquatic environments where uneaten feed does notsit around, as it might with terrestrial farming, but instead getswashed away and therefore wasted), cause deterioration in water qualitydue to pollution, and/or affect the health of fish by weakening immunesystems and increasing susceptibility to infections.

Conventionally, aquaculture companies employ various strategies forincreasing feeding management efficiency, such as by hiring additional,experienced employees (which may be difficult to find) and byimplementing automated feed distribution technologies. However, feedingtimes and amounts are often performed based at least in substantial partbased on visual determinations made by individual employees, whoseperformances will be variable amongst different individuals and whoseindividual performances are also likely variable on a day-to-day basis.Feed distribution systems may be automated to control and monitor feedfor individuals or groups of animals, reducing or eliminating the needfor extra labor and feeding costs traditionally associated with humanperformance of feeding tasks. Further, the feed distribution systems maysometimes utilize sensor systems to monitor fish behavior, such as toidentify hunger levels and determine behavior during feeding, and tofurther determine an amount of feed required in order to reach satiationfor fish populations.

However, the performance of such automated appetite identification andrelated sensor systems will have inherent uncertainties in theirperformance due to the uncertainty of detecting changes in appetite offish and further by nature of the environments in which they aredeployed. For example, aquaculture stock is, with few exceptions, oftenheld underwater and therefore more difficult to observe than animals andplants cultured on land. Further, aquaculture is commonly practiced inopen, outdoor environments and therefore exposes farmed animals, farmstaff, and farming equipment to factors that are, at least partially,beyond the control of operators. Such factors include, for example,variable and severe weather conditions, changes to water conditions,dissolved oxygen levels, turbidity, interference with farm operationsfrom predators, and the like.

To improve the precision and accuracy of feeding appetite forecastingand decreasing the uncertainties associated with conventional appetiteprediction systems, FIGS. 1-8 describe techniques for providing afeeding appetite forecast that includes providing a first feedingparameter data set associated with a first feeding parameter to a firstfeeding appetite forecast model. A second feeding parameter data setassociated with a second feeding parameter is provided to a secondfeeding appetite forecast model different from the first forecast model.Subsequently, the first feeding appetite forecast model is adaptivelyweighted with a first weighting factor relative to a second weightingfactor for the second feeding appetite forecast model. An aggregatedappetite score is determined based on a combination of the first feedingappetite forecast model using the first weight factor and the secondfeeding appetite forecast model using the second weight factor. In thismanner, different feeding appetite predictions from different feedingappetite forecast models are adaptively weighted and combined into anaggregated appetite score that is more accurate than would beindividually provided by each feeding appetite forecast model by itself

FIG. 1 is a diagram of a system 100 for implementing feeding appetiteforecasting in accordance with some embodiments. In various embodiments,the system 100 includes a plurality of sensor systems 102 that are eachconfigured to monitor and generate data associated with the environment104 within which they are placed. As shown, the plurality of sensorsystems 102 includes a first sensor system 102 a positioned below thewater surface 106 and including a first set of one or more sensors. Thefirst set of one or more sensors are configured to monitor theenvironment 104 below the water surface 106 and generate data associatedwith a first feeding parameter. It will be appreciated that feedingparameters, in various embodiments, include one or more parameterscorresponding to the environment 104 within which the one or moresensors are positioned and may be measured (or otherwise captured anddetected) to generate parameter data sets 108 to be used in forecastingmodels. Accordingly, in some embodiments, the first sensor system 102 agenerates a first parameter data set 108 a and communicates the firstparameter data set 108 a to a processing system 110 for storage,processing, and the like.

The plurality of sensor systems 102 also includes a second sensor system102 b positioned below the water surface 106 and including a second setof one or more sensors. The second set of one or more sensors areconfigured to monitor the environment 104 below the water surface 106and generate data associated with a second feeding parameter. It will beappreciated that feeding parameters, in various embodiments, include oneor more parameters corresponding to the environment 104 within which theone or more sensors are positioned and may be measured (or otherwisecaptured and detected) to generate parameter data sets 108 to be used inforecasting models. Accordingly, in some embodiments, the second sensorsystem 102 b generates a second parameter data set 108 b andcommunicates the second parameter data set 108 b to the processingsystem 110 for storage, processing, and the like. It should berecognized that the plurality of sensor systems 102 are not limited tobeing positioned under the water surface 106. In some embodiments, theplurality of sensor systems 102 also includes a third sensor system 102c positioned at or above the water surface 106 and including a third setof one or more sensors. The third set of one or more sensors areconfigured to monitor the environment 104 at or above the water surface106 and generate data associated with a third feeding parameter.Accordingly, in some embodiments, the third sensor system 102 cgenerates a third parameter data set 108 c and communicates the thirdparameter data set 108 c to the processing system 110 for storage,processing, and the like.

The processing system 110 includes one or more processors 112 coupledwith a communications bus 114 for processing information. In variousembodiments, the one or more processors 112 include, for example, one ormore general purpose microprocessors or other hardware processors. Theprocessing system 110 also includes one or more storage devices 116communicably coupled to the communications bus 114 for storinginformation and instructions. For example, in some embodiments, the oneor more storage devices 116 includes a magnetic disk, optical disk, orUSB thumb drive, and the like for storing information and instructions.In various embodiments, the one or more storage devices 116 alsoincludes a main memory, such as a random-access memory (RAM), cacheand/or other dynamic storage devices, coupled to the communications bus114 for storing information and instructions to be executed by the oneor more processors 112. The main memory may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by the one or more processors 112. Suchinstructions, when stored in storage media accessible by the one or moreprocessors 112, render the processing system 110 into a special-purposemachine that is customized to perform the operations specified in theinstructions. By way of non-limiting example, in various embodiments,the processing system 110 may be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer, mobile computing or communication device, or other form ofcomputing or telecommunications device that is capable of communicationand that has sufficient processor power and memory capacity to performthe operations described herein.

The processing system 110 also includes a communications interface 118communicably coupled to the communications bus 114. The communicationsinterface 118 provides a multi-way data communication couplingconfigured to send and receive electrical, electromagnetic or opticalsignals that carry digital data streams representing various types ofinformation. In various embodiments, the communications interface 118provides data communication to other data devices via, for example, anetwork 120. For example, in some embodiments, the processing system 110may be configured to communicate with one or more remote platforms 122according to a client/server architecture, a peer-to-peer architecture,and/or other architectures via a network 116. Remote platform(s) 122 maybe configured to communicate with other remote platforms via theprocessing system 110 and/or according to a client/server architecture,a peer-to-peer architecture, and/or other architectures via the network116. Users may access system 100 via remote platform(s) 122.

A given remote platform 122 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable a user associated with the givenremote platform 122 to interface with system 100 and/or externalresources 124, and/or provide other functionality attributed herein toremote platform(s) 122. External resources 122 may include sources ofinformation outside of system 100, external entities participating withsystem 100, and/or other resources. In some implementations, some or allof the functionality attributed herein to external resources 122 may beprovided by resources included in system 100.

In some embodiments, the processing system 110, remote platform(s) 122,and/or one or more external resources 124 may be operatively linked viaone or more electronic communication links. For example, such electroniccommunication links may be established, at least in part, via thenetwork 120. It will be appreciated that this is not intended to belimiting, and that the scope of this disclosure includes implementationsin which computing platform(s) 102, remote platform(s) 104, and/orexternal resources 122 may be operatively linked via some othercommunication media. Further, in various embodiments, the processingsystem 110 is configured to send messages and receive data, includingprogram code, through the network 120, a network link (not shown), andthe communications interface 118. For example, a server 126 may beconfigured to transmit or receive a requested code for an applicationprogram through via the network 120, with the received code beingexecuted by the one or more processors 112 as it is received, and/orstored in storage device 116 (or other non-volatile storage) for laterexecution.

In various embodiments, the processing system 110 receives one or moreof the parameter data sets 108 and stores the parameter data sets 108 atthe storage device 116 for processing. As described in more detail belowwith respect to FIGS. 2-8, the processing system 110 provides a firstfeeding parameter data set (e.g., first parameter data set 108 a) to afirst feeding appetite forecast model and further provides a secondfeeding parameter data set (e.g., second parameter data set 108 b) to asecond feeding appetite forecast model different from the first forecastmodel. In various embodiments, the first feeding appetite forecast modelreceives a data set corresponding to measurements for at least a firstfeeding parameter related to feeding appetite forecasting. Similarly,the second feeding appetite forecast model receives a data setcorresponding to measurements for at least a second feeding parameterrelated to feeding appetite forecasting. By way of non-limiting example,in some embodiments, a feeding parameter describes fish position withinthe water below the water surface 106.

Data corresponding to such a feeding parameter may be utilized as inputby a feeding appetite forecast model to generate a description of apossible hunger level to be exhibited by the fish within the water for afuture time period (i.e., a feeding appetite prediction). Subsequently,the processing system 110 adaptively weights the first feeding appetiteforecast model with a first weighting factor relative to a secondweighting factor for the second feeding appetite forecast model in orderto determine an aggregated appetite score based on a combination of thefirst feeding appetite forecast model using the first weight factor andthe second feeding appetite forecast model using the second weightfactor. In this manner, the processing system 110 provides a weightingto different feeding appetite predictions from different feedingappetite forecast models and combines them into an aggregated appetitescore that is more accurate than would be individually provided by eachfeeding appetite forecast model by itself

As described in more detail below with respect to FIGS. 2-8, byassigning weight factors to the feeding appetite forecast models andtheir respective feeding appetite forecasts, the system influences theforecast parameters utilized and therefore combine the forecastparameters in an advantageous way. The resulting aggregated appetitescore therefore indicates for multiple forecast parameters and forecastmodels and may have an improved prediction accuracy. For example, usingmultiple forecasts from multiple sources and combining them in anoptimal way based on a validation and comparison of model parameterswith reference data (e.g., weather data, weather forecasts,environmental conditions, and more as discussed in further detail below)provides an appetite forecast with an increased accuracy.

In various embodiments, the system 100 also includes a feed controlsystem (not shown) that receives a feed instruction signal (such asfeeding instruction signals 310 and 810 described in more detail belowwith respect to FIGS. 3 and 8) for modifying and/or guiding theoperations (e.g., dispensing of feed related to meal size, feeddistribution, meal frequency, feed rate, etc.) of feeding systemsincluding automatic feeders, feed cannons, and the like. It should berecognized that the feed instruction signal is not limited to anyparticular format and may include any representation including but notlimited to feeding instructions, user interface alerts, and the like. Aswill be appreciated, in various embodiments, users and operators may becapable of modifying their feeding strategy manually based on the feedinstruction signal. For example, in some embodiments, the operator mayprovide fewer feed pellets when the feed instruction signal provides asignal that appetite is low, such as via graphical user interface (GUI)displays, audible and visual alerts, and the like. In other embodiments,the feed instruction signal may be provided to an automatic feeder forcontrolling feeding operations in a manner that reduces or eliminatesmanual, human intervention. Accordingly, various parameters such asfeeding rate, feeding amount, feeding frequency, feeding timing, and thelike may be modified based on the feeding instruction signal.

Referring now to FIG. 2, illustrated is a diagram showing a system 200implementing two sets of different underwater sensors in accordance withsome embodiments. In various embodiments, the system 200 includes aplurality of sensor systems 202 that are each configured to monitor andgenerate data associated with the environment 204 within which they areplaced. As shown, the plurality of sensor systems 202 includes a firstsensor system 202 a positioned below the water surface 206 and includinga first set of one or more sensors. The first set of one or more sensorsare configured to monitor the environment 204 below the water surface206 and generate data associated with a first feeding parameter. Inparticular, the first sensor system 202 a of FIG. 2 includes one or morehydroacoustic sensors configured to observe fish behavior and capturemeasurements associated with feeding parameters related to fishappetite. For example, in various embodiments, the hydroacoustic sensorsare configured to capture acoustic data corresponding to the presence(or absence), abundance, distribution, size, and behavior of underwaterobjects (e.g., a population of fish 212 as illustrated in FIG. 2).

Such acoustic data measurements may therefore measure fish positionswithin the water to be used as an approximation of appetite. As usedherein, it should be appreciated that an “object” refers to anystationary, semi-stationary, or moving object, item, area, orenvironment in which it may be desired for the various sensor systemsdescribed herein to acquire or otherwise capture data of For example, anobject may include, but is not limited to, one or more fish, crustacean,feed pellets, predatory animals, and the like. However, it should beappreciated that the sensor measurement acquisition and analysis systemsdisclosed herein may acquire and/or analyze sensor data regarding anydesired or suitable “object” in accordance with operations of thesystems as disclosed herein.

The one or more hydroacoustic sensors of the first sensor system 202 aincludes one or more of a passive acoustic sensor and/or an activeacoustic sensor (e.g., an echo sounder and the like). Passive acousticsensors generally listen for remotely generated sounds (e.g., often atspecified frequencies or for purposes of specific analyses, such as fordetecting fish or feed in various aquatic environments) withouttransmitting into the underwater environment 204. In contrast, activeacoustic sensors conventionally include both an acoustic receiver and anacoustic transmitter that transmit pulses of sound (e.g., pings) intothe surrounding environment 204 and then listens for reflections (e.g.,echoes) of the sound pulses. It is noted that as sound waves/pulsestravel through water, it will encounter objects having differingdensities or acoustic properties than the surrounding medium (i.e., theunderwater environment 204) that reflect sound back towards the activesound source(s) utilized in active acoustic systems. For example, soundtravels differently through fish 212 (and other objects in the watersuch as feed pellets 214) than through water (e.g., a fish's air-filledswim bladder has a different density than water). Accordingly,differences in reflected sound waves from active acoustic techniques dueto differing object densities may be accounted for in the detection ofaquatic life and estimation of their individual sizes or total biomass.It should be recognized that although specific sensors are describedbelow for illustrative purposes, various hydroacoustic sensors may beimplemented in the systems described herein without departing from thescope of this disclosure.

In various embodiments, the first sensor system 202 a utilizes activesonar systems in which pulses of sound are generated using a sonarprojector including a signal generator, electro-acoustic transducer orarray, and the like. The active sonar system may further include abeamformer (not shown) to concentrate the sound pulses into an acousticbeam 216 covering a certain search angle 218. In some embodiments, thefirst sensor system 202 a measures distance through water between twosonar transducers or a combination of a hydrophone (e.g., underwateracoustic microphone) and projector (e.g., underwater acoustic speaker).The first sensor system 202 a includes sonar transducers (not shown) fortransmitting and receiving acoustic signals (e.g., pings). To measuredistance, one transducer (or projector) transmits an interrogationsignal and measures the time between this transmission and the receiptof a reply signal from the other transducer (or hydrophone). The timedifference, scaled by the speed of sound through water and divided bytwo, is the distance between the two platforms. This technique, whenused with multiple transducers, hydrophones, and/or projectorscalculates the relative positions of objects in the underwaterenvironment 204.

In other embodiments, the first sensor system 202 a includes an acoustictransducer configured to emit sound pulses into the surrounding watermedium. Upon encountering objects that are of differing densities thanthe surrounding water medium (e.g., the fish 212), those objects reflectback a portion of the sound towards the sound source (i.e., the acoustictransducer). Due to acoustic beam patterns, identical targets atdifferent azimuth angles will return different echo levels. Accordingly,if the beam pattern and angle to a target is known, this directivity maybe compensated for. In various embodiments, split-beam echosoundersdivide transducer faces into multiple quadrants and allow for locationof targets in three dimensions. Similarly, multi-beam sonar projects afan-shaped set of sound beams outward from the first sensor system 202 aand record echoes in each beam, thereby adding extra dimensions relativeto the narrower water column profile given by an echosounder. Multiplepings may thus be combined to give a three-dimensional representation ofobject distribution within the water environment 204.

In some embodiments, the one or more hydroacoustic sensors of the firstsensor system 202 a includes a Doppler system using a combination ofcameras and utilizing the Doppler effect to monitor the appetite ofsalmon in sea pens. The Doppler system is located underwater andincorporates a camera, which is positioned facing upwards towards thewater surface 206. In various embodiments, there is a further cameraintegrated in the transmission device normally positioned on thetop-right of the pen, monitoring the surface of the pen. The sensoritself uses the Doppler effect to differentiate pellets from fish. Thesensor produces an acoustic signal and receives the echo. The sensor istuned to recognize pellets and is capable of transmitting theinformation by radio link to the feed controller over extendeddistances. The user watches the monitor and determines when feedingshould be stopped. Alternatively, a threshold level of waste pellets canbe set by the operator, and the feeder will automatically switch offafter the threshold level is exceeded.

In other embodiments, the one or more hydroacoustic sensors of the firstsensor system 202 a includes an acoustic camera having a microphonearray (or similar transducer array) from which acoustic signals aresimultaneously collected (or collected with known relative time delaysto be able to use phase different between signals at the differentmicrophones or transducers) and processed to form a representation ofthe location of the sound sources. In various embodiments, the acousticcamera also optionally includes an optical camera.

Additionally, as illustrated in FIG. 2, the plurality of sensor systems202 includes a second sensor system 202 b positioned below the watersurface 206 and including a second set of one or more sensors. Thesecond set of one or more sensors are configured to monitor theenvironment 204 below the water surface 206 and generate data associatedwith a second feeding parameter. In particular, the second sensor system202 b of FIG. 2 includes one or more imaging sensors configured toobserve fish behavior and capture measurements associated with feedingparameters related to fish appetite. In various embodiments, the imagingsensors are configured to capture image data corresponding to, forexample, the presence (or absence), abundance, distribution, size, andbehavior of underwater objects (e.g., a population of fish 212 asillustrated in FIG. 2). Such image data measurements may therefore beused to identify fish positions within the water for approximation ofappetite. It should be recognized that although specific sensors aredescribed below for illustrative purposes, various imaging sensors maybe implemented in the systems described herein without departing fromthe scope of this disclosure.

In some embodiments, the imaging sensors of the second sensor system 202b includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the surrounding environment 204 below the water surface206, with each camera capturing a sequence of images (e.g., videoframes) of the environment 204 and any objects in the environment. Invarious embodiments, each camera has a different viewpoint or pose(i.e., location and orientation) with respect to the environment.Although FIG. 2 only shows a single camera for ease of illustration anddescription, persons of ordinary skill in the art having benefit of thepresent disclosure should appreciate that the second sensor system 202 bcan include any number of cameras and which may account for parameterssuch as each camera's horizontal field of view, vertical field of view,and the like. Further, persons of ordinary skill in the art havingbenefit of the present disclosure should appreciate that the secondsensor system 202 b can include any arrangement of cameras (e.g.,cameras positioned on different planes relative to each other,single-plane arrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the second sensor system202 b includes a first camera (or lens) having a particular field ofview 220 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 204 or atleast a portion thereof. For the sake of clarity, only the field of view220 for a single camera is illustrated in FIG. 2. In variousembodiments, the imaging sensors of the second sensor system 202 bincludes at least a second camera having a different but overlappingfield of view (not shown) relative to the first camera (or lens). Imagesfrom the two cameras therefore form a stereoscopic pair for providing astereoscopic view of objects in the overlapping field of view. Further,it should be recognized that the overlapping field of view is notrestricted to being shared between only two cameras. For example, atleast a portion of the field of view 220 of the first camera of thesecond sensor system 202 b may, in some embodiments, overlap with thefields of view of two other cameras to form an overlapping field of viewwith three different perspectives of the environment 204.

In some embodiments, the imaging sensors of the second sensor system 202b includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 204. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the second sensor system 202 bincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras. However, it should berecognized that the operations described herein may similarly beimplemented with any type of imaging sensor without departing from thescope of this disclosure. For example, in various embodiments, theimaging sensors of the second sensor system 202 may include, but are notlimited to, any of a number of types of optical cameras (e.g., RGB andinfrared), thermal cameras, range- and distance-finding cameras (e.g.,based on acoustics, laser, radar, and the like), stereo cameras,structured light cameras, ToF cameras, CCD-based cameras, CMOS-basedcameras, machine vision systems, light curtains, multi- andhyper-spectral cameras, thermal cameras, and the like.

Additionally, as illustrated in FIG. 2, the plurality of sensor systems202 includes a third sensor system 202 c positioned below the watersurface 206 and including a third set of one or more sensors. The thirdset of one or more sensors are configured to monitor the environment 204below the water surface 206 and generate data associated with areference parameter. In particular, the third sensor system 202 c ofFIG. 2 includes one or more environmental sensors configured to capturemeasurements associated with the environment 204 within which the system200 is deployed. As described in further detail below, in variousembodiments, the environmental sensors of the third sensor system 202 cgenerate environmental data that serves as reference data forimplementing the dynamic weighting of various forecasts from a pluralityof feeding appetite forecast models. For example, in one embodiment, theenvironmental sensors of the third sensor system 202 c includes aturbidity sensor configured to measure an amount of light scattered bysuspended solids in the water. Turbidity is a measure of the degree towhich water (or other liquids) changes in level of its transparency dueto the presence of suspended particulates (e.g., by measuring an amountof light transmitted through the water).

In general, the more total suspended particulates or solids in water,the higher the turbidity and therefore murkier the water appears. Aswill be appreciated, a variable parameter such as variance in theturbidity of a liquid medium will affect the accuracy of image-basedmeasurements and accordingly the accuracy of prediction models thatconsume such images as input. Accordingly, by measuring turbidity as areference parameter, the detected intensity of turbidity measurementsmay be utilized as a basis for determining a weighting that animage-based prediction model should be given relative to other non-imagebased prediction models, as described in more detail below. It should berecognized that although FIG. 2 is described in the specific context ofa turbidity sensor, the third sensor system 202 c may include any numberof and any combination of various environmental sensors withoutdeparting from the scope of this disclosure.

The first sensor system 202 a and the second sensor system 202 b eachgenerate a first feeding parameter data set 208 a and a second feedingparameter data set 208 b, respectively. In the context of FIG. 2, thefirst feeding parameter includes acoustic data. Such acoustic data mayinclude any acoustics-related value or other measurablefactor/characteristic that is representative of at least a portion of adata set that describes the presence (or absence), abundance,distribution, size, and/or behavior of underwater objects (e.g., apopulation of fish 212 as illustrated in FIG. 2). For example, theacoustic data may include acoustic measurements indicative of therelative and/or absolute locations of individual fish of the populationof fish 212 within the environment 204. It should be recognized thatalthough the first feeding parameter has been abstracted and describedhere generally as “acoustic data” for ease of description, those skilledin the art will understand that acoustic data (and therefore the firstfeeding parameter data set 208 a corresponding to the acoustic data) mayinclude, but is not limited to, any of a plurality of acousticsmeasurements, acoustic sensor specifications, operational parameters ofacoustic sensors, and the like.

In the context of FIG. 2, the second feeding parameter includes imagedata. Such image data may include any image-related value or othermeasurable factor/characteristic that is representative of at least aportion of a data set that describes the presence (or absence),abundance, distribution, size, and/or behavior of underwater objects(e.g., a population of fish 212 as illustrated in FIG. 2). For example,the image data may include camera images capturing measurementsrepresentative of the relative and/or absolute locations of individualfish of the population of fish 212 within the environment 204. It shouldbe recognized that although the second feeding parameter has beenabstracted and described here generally as “image data” for ease ofdescription, those skilled in the art will understand that image data(and therefore the second feeding parameter data set 208 b correspondingto the image data) may include, but is not limited to, any of aplurality of image frames, extrinsic parameters defining the locationand orientation of the image sensors, intrinsic parameters that allow amapping between camera coordinates and pixel coordinates in an imageframe, camera models, operational parameters of the image sensors (e.g.,shutter speed), depth maps, and the like.

Similarly, in the context of FIG. 2, the reference parameter includesenvironmental data. Such environmental data may include any measurementrepresentative of the environment 204 within which the environmentalsensors are deployed. For example, the environmental data (and thereforethe reference parameter data set 208 c corresponding to theenvironmental data) may include, but is not limited to, any of aplurality of water turbidity measurements, water temperaturemeasurements, metocean measurements, weather forecasts, air temperature,dissolved oxygen, current direction, current speeds, and the like.

In various embodiments, the processing system 210 receives one or moreof the data sets 208 (e.g., first feeding parameter data set 208 a, thesecond feeding parameter data set 208 b, and the reference parameterdata set 208 c) via, for example, wired-telemetry, wireless-telemetry,or any other communications links for processing. The processing system210 provides the first feeding parameter data set 208 a to a firstfeeding appetite forecast model 222 a. The processing system 210 alsoprovides the second feeding parameter data set 208 b to a second feedingappetite forecast model 222 b different from the first feeding appetiteforecast model 222 a. In various embodiments, the first feeding appetiteforecast model 222 a receives the acoustic data of the first feedingparameter data set 208 a as input and generates a first feeding appetiteforecast 224 a.

By way of non-limiting example, in some embodiments, the first feedingappetite forecast model 222 a utilizes acoustic data related to fishposition within the water below the water surface 206 as a proxy forappetite (as appetite is a value which cannot be directly measured andmust be inferred) in generating the first feeding appetite forecast 224a. In various embodiments, the first feeding appetite forecast 224 a isa description of a possible hunger level to be exhibited by thepopulation of fish 212 within the water for a future time period (i.e.,a feeding appetite prediction).

In various embodiments, such as described below in more detail withrespect to FIG. 3, the first feeding appetite forecast 224 a generatedby the first feeding appetite forecast model 222 a is represented as anumerical score within a numerical indexing system. However, thoseskilled in the art will recognize that such a numerical representationof appetite and appetite prediction is provided as a non-limitingexample for ease of illustration. As used herein, the term “feedingappetite forecast” refers to any representation (e.g., including bothquantitative and qualitative) or other description of a parameter (whichmay be based on sensor measurements, derived from sensor measurements,input based on human observations, and the like).

Additionally, it should be recognized that a “feeding appetite forecast”is not limited to the output of forecast models and in some embodimentsmay include, for example, human-based inputs such as textual entries ina spreadsheet indicating hunger status (e.g., hungry vs. not hungry),numerical descriptors of fish activity (e.g., appetite ranking on a 0-10scale based on feeder personal experience such as feeding appetiteforecast 802 b of FIG. 8), and the like. In other embodiments, such asdescribed below in more detail with respect to FIG. 8, a “feedingappetite forecast” may also include (model-based-output or otherwise) araw numerical quantification without any relation to a baselinereference (e.g., feeding appetite forecast 802 a), a color-codeddescriptor (e.g., feeding appetite forecast 802 c), a percentagequantification of total biomass positioned at a location within thewater that is indicative of hunger (e.g., biomass within an upper ⅓ ofsea cage volume, such as feeding appetite forecast 802 d), instructionsto change feeding rate or total amount (e.g., feeding appetite forecast802 e) and the like.

Further, it should be noted that although the various operations areprimarily described here in the context of forecasting (or predicting)appetite for a future time period, the operations described herein maysimilarly be applied to description for a prior time period ordescription of a feeding appetite metric in real-time (i.e., for acurrent time period) without departing from the scope of thisdisclosure. Accordingly, as used herein, a “forecast” or “prediction”(e.g., in the context of feeding) refers to describing, eitherqualitatively or quantitatively, any proxy related to estimated level ofappetite (or in the inverse, satiation), as appetite levels aregenerally not directly quantifiable or otherwise measurable in discreteunit terms (unlike, for example, temperature or humidity which haveconventional units of measurement).

The processing system 210 also provides the second feeding parameterdata set 208 b to a second feeding appetite forecast model 222 b. Invarious embodiments, the second feeding appetite forecast model 222 breceives the image data of the second feeding parameter data set 208 bas input and generates a second feeding appetite forecast 224 b. By wayof non-limiting example, in some embodiments, the second feedingappetite forecast model 222 b utilizes image data related to fishposition within the water below the water surface 206 as an appetiteproxy for generating the second feeding appetite forecast 224 b. Invarious embodiments, the second feeding appetite forecast 224 a is adescription of a possible hunger level to be exhibited by the populationof fish 212 within the water for a future time period (i.e., a feedingappetite prediction).

Subsequently, the processing system 210 adaptively weights the firstfeeding appetite forecast 224 a of the first feeding appetite forecastmodel 222 a with a first weighting factor relative to a second weightingfactor for a second feeding appetite forecast 224 b of the secondfeeding appetite forecast model 222 b in order to determine anaggregated appetite score 226 based on a combination of the firstfeeding appetite forecast model 222 a using the first weight factor andthe second feeding appetite forecast model 222 b using the second weightfactor. In this manner, and as described in more detail below, theprocessing system 210 provides a weighting to different feeding appetitepredictions from different feeding appetite forecast models and combinesthem into an aggregated appetite score 226 that is more accurate thanwould be individually provided by each feeding appetite forecast modelby itself

In one embodiment, with respect to FIG. 3 and with continued referenceto FIG. 2, a plurality of feeding appetite forecast models (e.g., thefirst feeding appetite model 222 a and the second feeding appetite model222 b) receive their respective inputs (e.g., the feeding parameter datasets 208 a and 208 b) and generate a plurality of feeding appetiteforecasts for a number N of different feeding appetite forecast models(not shown) under a first set of environmental conditions in which theweather is sunny, waters are clean, and waves are choppy. As illustratedin FIG. 3, a first feeding appetite model using acoustic data as input(e.g., first feeding appetite model 222 a of FIG. 2) generates a firstfeeding appetite forecast (e.g., first feeding appetite forecast 224 a),as represented by a first model score 302 a in FIG. 3. A second feedingappetite model using image data as input (e.g., second feeding appetitemodel 222 b of FIG. 2) generates a second feeding appetite forecast(e.g., second feeding appetite forecast 224 b), as represented by asecond model score 302 b in FIG. 3.

As illustrated, the first model score 302 a is 80 on an example scale of0-100, which is indicative of a relatively high level of forecastedappetite (e.g., as embodied in a forecasted appetite score with a scoreof 0 representing that every single individual in the population of fish212 is expected to avoid any and all feed pellets administered and ascore of 100 representing that the population of fish 212 is ravenous)for a given time period. The second model score 302 b is 8.5 on adifferent example scale of 0.0-10.0, which is also similarly indicativeof a relatively high level of forecasted appetite (e.g., as embodied ina forecasted appetite score with a score of 0.0 representing that everysingle individual in the population of fish 212 is expected to avoid anyand all feed pellets administered and a score of 10.0 representing thatthe population of fish 212 is ravenous) for a given time period.

As previously discussed in more detail with respect to FIG. 2, theenvironmental sensors of the third sensor system 202 c generateenvironmental data that serves as reference data for implementing thedynamic weighting of various forecasts from a plurality of feedingappetite forecast models. Although the first model score 302 a and thesecond model score 302 b both generally indicate a relatively high levelof forecasted appetite, the two scores are represented in differing unitscales (e.g., a first scale having one hundred points from 0-100 and asecond scale having ten points from 0.0-10.0). Accordingly, in variousembodiments, the processing system 210 optionally (as indicated by thedotted lines) normalizes each of the model scores 302 a through 302N toa common scoring scale.

For example, as illustrated in FIG. 3, the processing system 210normalizes the model scores based on a one hundred point scale togenerate a first normalized model score 304 a of 80 associated with thefirst model score 302 a of 80. Similarly, the processing system 210normalizes the second model score 302 b of 8.5 based on the same onehundred point scale to generated a second normalized model score 304 bof 85. Based on a comparison of the first and second feeding parameterdata sets 208 a, 208 b relative to the measured reference (e.g.,environmental) data such as the reference parameter data set 208 c, theprocessing system 210 assigns a first weighting factor w₁ of 0.4 to thefirst feeding appetite forecast model (e.g., the first feeding appetitemodel 222 a of FIG. 2) and its associated first model score 302 a andfirst normalized model score 304 a. Additionally, in this example whereN=2 for using two different models in forecasting appetite, theprocessing system 210 also assigns a second weighting factor w₂ of 0.6to the second appetite forecast model (e.g., the second feeding appetitemodel 222 b of FIG. 2) and its associated second model score 302 b andsecond normalized model score 304 b.

The processing system 210 assigns this relative weighting with the firstweighting factor w₁ of 0.4 for the first feeding appetite forecast model(based on acoustic data) and the second weighting factor w₂ of 0.6 forthe second feeding appetite forecast model (based on image data) due toa first set of environmental conditions (e.g., using environmental datafrom the environmental sensors to measure conditions for a current timeor to forecast for a future time period) in which the weather is sunny,waters are clean, but waves are choppy. At a high level of abstraction,the processing system 210 determines that the image data captured by thesecond sensor system 202 b (which is positively influenced by, forexample, ambient light due to the sunny conditions and clear waters)will be of a relatively better quality than the acoustic data capturedby the first sensor system 202 a (which is negatively influenced by, forexample, background sounds due to the choppy waves which decrease thesignal-to-noise ratio of acoustic data). Subsequently, the processingsystem 210 applies the assigned weighting factors w₁, w₂ to the firstnormalized model score 304 a and the second normalized model score 304b, respectively, to generate a first weighted model score 306 a of 32and a second weighted model score 306 b of 51. Further, the processingsystem 210 combines these two weighted model scores 306 a, 306 b togenerate a weighted, aggregated appetite score 308 of 83 and therebyintegrates data from multi-sensor systems to provide an appetiteforecast.

In various embodiments, the systems described herein may optionallygenerate a feeding instruction signal 310 (as represented by the dottedline box) based at least in part on the aggregated appetite score 308that instructs a user and/or automated feeding system regarding specificactions to be taken in accordance to the feeding appetite prediction(e.g., as quantified by the aggregated appetite score 308). It will beappreciated that the feeding instruction signal 310 is not limited toany particular format and in various embodiments may be converted to anyappropriate format to be compatible for intended usage, includingcontrol signals for modifying operations of feeding systems and displaycommands for presenting visual directions regarding feeding. Suchformats for the feeding instruction signal 310 include, by way ofnon-limiting example, a stop signal, a color-coded user interfacedisplay, a specific feed rate that should be administered, a total feedvolume that should be administered, and the like.

As will be appreciated, environmental conditions often vary and therelative accuracy of data gathered by different sensor systems will alsovary over time. In another embodiment, with respect to FIG. 3 and withcontinued reference to FIG. 2, a plurality of feeding appetite forecastmodels (e.g., the first feeding appetite model 222 a and the secondfeeding appetite model 222 b) receive their respective inputs (e.g., thefeeding parameter data sets 208 a and 208 b) and generate a plurality offeeding appetite forecasts for a number N of different feeding appetiteforecast models (not shown) under a second set of environmentalconditions in which the weather is dark (e.g., at dusk or dawn), watershave increased turbidity levels, but waves are calm. As illustrated inFIG. 3, a first feeding appetite model using acoustic data as input(e.g., first feeding appetite model 222 a of FIG. 2) generates a firstfeeding appetite forecast (e.g., first feeding appetite forecast 224 a),as represented by a first model score 312 a in FIG. 3. A second feedingappetite model using image data as input (e.g., second feeding appetitemodel 222 b of FIG. 2) generates a second feeding appetite forecast(e.g., second feeding appetite forecast 224 b), as represented by asecond model score 312 b in FIG. 3.

As illustrated, the first model score 312 a is 80 on an example scale of0-100, which is indicative of a relatively high level of forecastedappetite (e.g., as embodied in a forecasted appetite score with a scoreof 0 representing that every single individual in the population of fish212 is expected to avoid any and all feed pellets administered and ascore of 100 representing that the population of fish 212 is ravenous)for a given time period. The second model score 312 b is 4.0 on adifferent example scale of 0.0-10.0, which is also similarly indicativeof a relatively high level of forecasted appetite (e.g., as embodied ina forecasted appetite score with a score of 0.0 representing that everysingle individual in the population of fish 212 is expected to avoid anyand all feed pellets administered and a score of 10.0 representing thatthe population of fish 212 is ravenous) for a given time period.

As previously discussed in more detail with respect to FIG. 2, theenvironmental sensors of the third sensor system 202 c generateenvironmental data that serves as reference data for implementing thedynamic weighting of various forecasts from a plurality of feedingappetite forecast models. The first model score 312 a and the secondmodel score 312 b are represented in differing unit scales (e.g., afirst scale having one hundred points from 0-100and a second scalehaving ten points from 0.0-10.0). Accordingly, in various embodiments,the processing system 210 optionally (as indicated by the dotted lines)normalizes each of the model scores 312 a through 312N to a commonscoring scale.

For example, as illustrated in FIG. 3, the processing system 210normalizes the model scores based on a one hundred point scale togenerate a first normalized model score 314 a of 80 associated with thefirst model score 302 a of 80. Similarly, the processing system 210normalizes the second model score 312 b of 4.0 based on the same onehundred point scale to generated a second normalized model score 314 bof 40. As previously discussed in more detail with respect to FIG. 2,the environmental sensors of the third sensor system 202 c generateenvironmental data that serves as reference data for implementing thedynamic weighting of various forecasts from a plurality of feedingappetite forecast models. In contrast to the first set of environmentalconditions previously described, the second set of environmentalconditions describes dark weather (e.g., at dusk or dawn), waters haveincreased turbidity levels, but waves are calm. Accordingly, based on acomparison of the first and second feeding parameter data sets 208 a,208 b relative to the measured reference (e.g., environmental) data suchas the reference parameter data set 208 c, the processing system 210assigns a first weighting factor w₁ of 0.9 to the first feeding appetiteforecast model (e.g., the first feeding appetite model 222 a of FIG. 2)and its associated first model score 312 a and first normalized modelscore 314 a. Additionally, in this example where N=2 for using twodifferent models in forecasting appetite, the processing system 210 alsoassigns a second weighting factor w₂ of 0.1 to the second appetiteforecast model (e.g., the second feeding appetite model 222 b of FIG. 2)and its associated second model score 312 b and second normalized modelscore 314 b.

At a high level of abstraction, the processing system 210 determinesthat the acoustic data captured by the first sensor system 202 a (whichis positively influenced by, for example, calm waters) will haveimproved quality relative to acoustic data captured by the same firstsensor system 202 a under less favorable conditions (e.g., the first setof environmental conditions previously described. Additionally, theprocessing system 210 determines that the image data captured by thesecond sensor system 202 b (which is negatively influenced by, forexample, dark ambient light conditions and turbid waters) will havedegraded quality (such as due to inaccuracies arising from visibilityissues) relative to image data captured by the same second sensor system202 b under more favorable conditions (e.g., the first set ofenvironmental conditions previously described).

Accordingly, the processing system 210 assigns a relative weighting withthe first weighting factor w₁ of 0.9 for the first feeding appetiteforecast model (based on acoustic data) and the second weighting factorw₂ of 0.1 for the second feeding appetite forecast model (based on imagedata) due to the second set of environmental conditions and discountsthe image-based, second feeding appetite forecast model that is expectedto be less accurate in murky waters. Subsequently, the processing system210 applies the assigned weighting factors w₁, w₂ to the firstnormalized model score 314 a and the second normalized model score 314b, respectively, to generate a first weighted model score 316 a of 72and a second weighted model score 316 b of 4. Further, the processingsystem 210 combines these two weighted model scores 316 a, 316 b togenerate a weighted, aggregated appetite score 318 of 76 and therebyintegrates data from multi-sensor systems (some sensors and predictivemodels being more accurate than others, sometimes in all instances orsometimes depending on variable factors such as the environment)measuring fish positions as an approximation of appetite to provide anappetite forecast.

In various embodiments, the systems described herein may optionallygenerate a feeding instruction signal 320 (as represented by the dottedline box) based at least in part on the aggregated appetite score 318that instructs a user and/or automated feeding system regarding specificactions to be taken in accordance to the feeding appetite prediction(e.g., as quantified by the aggregated appetite score 318). It will beappreciated that the feeding instruction signal 320 is not limited toany particular format and in various embodiments may be converted to anyappropriate format to be compatible for intended usage, includingcontrol signals for modifying operations of feeding systems and displaycommands for presenting visual directions regarding feeding. Suchformats for the feeding instruction signal 320 include, by way ofnon-limiting example, a stop signal, a color-coded user interfacedisplay, a specific feed rate that should be administered, a total feedvolume that should be administered, and the like.

Referring now to FIG. 4, illustrated is a flow diagram of a method 400for providing a feeding appetite forecast in accordance with someembodiments. For ease of illustration and description, the method 400 isdescribed below with reference to and in an example context of thesystems 100 and 200 of FIG. 1 and FIG. 2, respectively. However, themethod 400 is not limited to this example context, but instead may beemployed for any of a variety of possible system configurations usingthe guidelines provided herein.

The method 400 begins at block 402 with the receipt by a first feedingappetite forecast model 222 a of a first feeding parameter data set 208a associated with a first feeding parameter. In various embodiments, theoperations of block 402 include providing, by a processing system, thefirst feeding parameter data set 208 a via a wireless or wiredcommunications link to the first feeding appetite forecast model 222 afor processing. For example, in the context of FIGS. 1 and 2, the sensorsystems 102, 202 communicate at least the first parameter data set 108a, 208 a to a processing system 110, 210 for storage at a local storagedevice 116. As illustrated in FIG. 2, the first feeding appetiteforecast model 222 a is executed locally using the same processingsystem 210 at which the first parameter data set 208 a is stored.Accordingly, the first parameter data set 208 a may be so provided tothe first appetite forecast model 222 a by transmitting one or more datastructures to processors 112 via a wireless or wired link (e.g.,communications bus 114) for processing. It should be noted that thefirst parameter data set and the first appetite forecast model do notneed to be stored and/or processed at the same device or system.Accordingly, in various embodiments, the providing of the firstparameter data set and its receipt by the first appetite forecast modelfor the operations of block 402 may be implemented in any distributedcomputing configuration (e.g., such as amongst the processing system110, network 120, remote platforms 122, external resources 124, andserver 126 of FIG. 1).

In at least one embodiment, the first parameter data set 108 a, 208 aincludes data corresponding to measurements for at least a first feedingparameter related to feeding appetite forecasting. For example, withreference to FIG. 2, the first feeding parameter includes acoustic datacorresponding to the presence (or absence), abundance, distribution,size, and behavior of underwater objects (e.g., a population of fish 212as illustrated in FIG. 2). Such acoustic data measurements may thereforemeasure fish positions within the water to be used as an approximationof appetite. Although described here in the context of acoustic datacharacterizing physical properties of the population of fish 212 (e.g.,location of the fish within the underwater environment 204), acousticdata related to physical properties of other underwater objects such asfeed 214 may also be measured for the first parameter data set 208 a.

Additionally, in various embodiments, acoustic data related to behaviorof underwater objects may also be measured for the first parameter dataset 208 a. In the context of feeding behavior, a hydroacoustic sensor ofthe first sensor system 202 a may monitor noises generated by thepopulation of fish 212 during feeding (e.g., chomping noises resultingfrom jaw movement while the fish eat) as indicators of appetite.Similarly, in the context of swimming behavior, a hydroacoustic sensorof the first sensor system 202 may monitor movement noises generated bythe population of fish during feeding (e.g., noises resulting fromswimming motion towards or away from feed pellets 214) such that areduction in noise may be indicative of reduced fish appetites as theyswim away from the feed pellets 214.

The method 400 continues at block 404 with the receipt by a secondfeeding appetite forecast model 222 b (that is different from the firstforecast model) of a second feeding parameter data set 208 b associatedwith a second feeding parameter. In various embodiments, the operationsof block 404 include providing, by a processing system, the secondfeeding parameter data set 208 b via a wireless or wired communicationslink to the second feeding appetite forecast model 222 b for processing.For example, in the context of FIGS. 1 and 2, the sensor systems 102,202 communicate at least the second parameter data set 108 b, 208 b to aprocessing system 110, 210 for storage at a local storage device 116. Asillustrated in FIG. 2, the second feeding appetite forecast model 222 bis executed locally using the same processing system 210 at which thesecond parameter data set 208 b is stored. Accordingly, the secondparameter data set 208 b may be so provided to the second appetiteforecast model 222 b by transmitting one or more data structures toprocessors 112 via a wireless or wired link (e.g., communications bus114) for processing. It should be noted that the second parameter dataset and the second appetite forecast model do not need to be storedand/or processed at the same device or system. Accordingly, in variousembodiments, the providing of the second parameter data set and itsreceipt by the second appetite forecast model for the operations ofblock 404 may be implemented in any distributed computing configuration(e.g., such as amongst the processing system 110, network 120, remoteplatforms 122, external resources 124, and server 126 of FIG. 1).

In at least one embodiment, the second parameter data set 108 b, 208 bincludes data corresponding to measurements for at least a secondfeeding parameter related to feeding appetite forecasting. For example,with reference to FIG. 2, the second feeding parameter includes imagedata corresponding to the presence (or absence), abundance,distribution, size, and behavior of underwater objects (e.g., apopulation of fish 212 as illustrated in FIG. 2). Such image data may beanalyzed using various image analysis techniques to identify variousphysical properties associated with the population of fish 212 such asfish positions within the water, depth within the water, estimatedbiomass, biomass location within the water, and the like to be used asan approximation of appetite. Further, image data related to propertiesof other underwater objects may also be measured for the secondparameter data set 208 b.

Additionally, in various embodiments, image data related to behavior ofunderwater objects may also be measured for the second parameter dataset 208 b. In the context of feeding behavior, an image sensor of thesecond sensor system 202 b may identify swimming behavior of thepopulation of fish 212 during feeding as indicators of appetite. Forexample, congregation of fish close to and in a circling patternproximate a physical source of feed pellets 214 may indicate a higherlevel of appetite than swimming behavior that is akin to a random walkpattern. Similarly, in the context of feed pellet behavior, an imagesensor of the second sensor system 202 b may monitor the falling pathsof feed pellets 214 such that an increase in amount of feed pellets 214that pass the population of fish 212 without being eaten (e.g., washedaway by water currents or falls out of an enclosure within which thepopulation of fish 212 are positioned) may be indicative of reduced fishappetites.

It should be recognized that although feeding appetite forecasting waspreviously described with respect to FIGS. 1-4 in the context underwateracoustic sensors, underwater image sensors, and underwater environmentalsensors, data may be collected by any of a variety of imaging andnon-imaging sensors. By way of non-limiting examples, in variousembodiments, the sensor systems may include various sensors local to thesite at which the fish are located (e.g., underwater telemetry devicesand sensors), sensors remote to the fish site (e.g., satellite-basedweather sensors such as scanning radiometers), various environmentalmonitoring sensors, active sensors (e.g., active sonar), passive sensors(e.g., passive acoustic microphone arrays), echo sounders,photo-sensors, ambient light detectors, accelerometers for measuringwave properties, salinity sensors, thermal sensors, infrared sensors,chemical detectors, temperature gauges, or any other sensor configuredto measure data that would have an influence on feeding appetite. Itshould be recognized that, in various embodiments, the sensor systemsutilized herein are not limited to underwater sensors and may includecombinations of a plurality of sensors at different locations, such asillustrated and described below with respect to FIG. 5. It should alsobe recognized that, in various embodiments, the sensor systems utilizedherein are not limited to sensors of differing parameter types. Forexample, in various embodiments, the sensor systems may include twodifferent image-data based sensor systems positioned at differentlocations (e.g., under water and above water as illustrated anddescribed below with respect to FIG. 5) and/or a plurality of differingreference sensors.

The operations of method 400 continues at block 406 with adaptivelyweighting the first feeding appetite forecast model with a firstweighting factor relative to a second weighting factor for the secondfeeding appetite forecast model. The operations of block 406 includesproviding measured reference data related to the first feeding parameterand the second feeding parameter. In various embodiments, providingmeasured reference data includes the third sensor system 202 cgenerating environmental data and providing the reference parameter dataset 208 c via a wireless or wired communications link to the processingsystem 210 for local storage and processing. It should be noted that thereference parameter data set 208 c does not need to be stored at thesame device or system at which the reference parameter data set 208 c isprocessed to determine weighting factors. Accordingly, in variousembodiments, the providing of the reference parameter data set 208 c andits receipt by the processing system 210 for the operations of block 406may be implemented in any distributed computing configuration (e.g.,such as amongst the processing system 110, network 120, remote platforms122, external resources 124, and server 126 of FIG. 1).

In at least one embodiment, the reference parameter data set 208 cincludes measured environmental reference data that is relevant to theprecision/accuracy of individual feeding appetite forecast models, therelative precision/accuracy between different feeding appetite forecastmodels, relative availability or reliability of data captured by any ofthe sensor systems discussed herein, and the like. Accordingly, invarious embodiments, the processing system 210 assigns, based on acomparison of the forecast models with the measured reference data, afirst weight factor to the first feeding appetite forecast model and asecond weight factor to the second feeding appetite forecast model.

With reference to FIGS. 2-3, the third sensor system 202 c of FIG. 2includes one or more environmental sensors configured to capturemeasurements associated with the environment 204 within which the system200 is deployed and generate environmental data that serves as referencedata for implementing the dynamic weighting of various forecasts from aplurality of feeding appetite forecast models. In one embodiment, theenvironmental sensors of the third sensor system 202 c includes aturbidity sensor configured to measure an amount of light scattered bysuspended solids in the water.

With reference to the first set of environmental conditions in FIG. 3,as determined based at least in part on environmental data from theenvironmental sensors to identify conditions in which the weather issunny plus waters are choppy but clean (e.g., based on the turbiditysensor measurements), the processing system 210 determines that theimage data captured by the second sensor system 202 b (which ispositively influenced by, for example, ambient light due to the sunnyconditions and clear waters) will be of a relatively better quality thanthe acoustic data captured by the first sensor system 202 a (which isnegatively influenced by, for example, background sounds due to thechoppy waves which decrease the signal-to-noise ratio of acoustic data).For example, in some embodiments, the processing system 210preferentially weights the image-based, second feeding appetite forecastmodel over the acoustics-based, first feeding appetite forecast modelbased on a clarity level of the water exceeding a predeterminedthreshold. Accordingly, the operations of block 406 include theprocessing system 210 assigning a relative weighting with the firstweighting factor w₁ of 0.4 for the first feeding appetite forecast modeland the second weighting factor w₂ of 0.6 for the second feedingappetite forecast model to account for differential forecasts in amulti-sensor system.

As will be appreciated, environmental conditions often vary and therelative accuracy of data gathered by different sensor systems will alsovary over time. With reference to the second set of environmentalconditions in FIG. 3, as determined based at least in part onenvironmental data from the environmental sensors to identify conditionsfor a second, future period of time in which the weather is expected tobe dark (e.g., at dusk or dawn), waters have increased turbidity levels,but waves are calm, the processing system 210 determines that theimage-based, second feeding appetite forecast model is expected to beless accurate in murky waters along with low ambient light levels. Thus,the processing system 210 will adaptively re-weight the weightingsassigned to different forecast models (relative to the weightingsassigned with respect to the first set of environmental conditions).Accordingly, the operations of block 406 include the processing system210 assigning a relative weighting with the first weighting factor w₁ of0.9 for the first feeding appetite forecast model (based on acousticdata) and the second weighting factor w₂ of 0.1 for the second feedingappetite forecast model (based on image data) to discount theimage-based, second feeding appetite forecast model that is expected tobe less accurate in dark and murky waters.

It should be recognized that although the weighting of differentappetite forecast models is described in the specific context ofvariable turbidity level measurements and forecasts, the operations ofblock 406 may involve weighting considerations including any number ofand any combination of parametric considerations (including data setscollected from a plurality of different environmental sensors includingphotodetectors to detect ambient light conditions and accelerometers tomeasure wave heights/swell periods, as referenced above but notexplicitly discussed) without departing from the scope of thisdisclosure. For example, such parametric environmental considerationsmay include data sets related to one or more water environmentparameters, meteorological parameters, forecasts of the same for futuretime periods, and the like.

Further, it will be appreciated that various non-environmentalconsiderations also have relevance as to the precision/accuracy ofindividual feeding appetite forecast models, the relativeprecision/accuracy between different feeding appetite forecast models,relative availability or reliability of data captured by any of thesensor systems discussed herein, and the like. In various embodiments,sensor measurements corresponding to different data sets (e.g., firstand second parameter data sets 108 a, 108 b) may be captured withdiffering temporal granularities. For example, in one hypotheticalsituation and with reference back to system 200, the image-based secondset of sensors may be configured to decrease a frame rate of imagecapture in response to low-bandwidth issues. However, theacoustics-based first set of sensors may not be subject to suchperformance throttling as audio files generally occupy less storagespace and consume less bandwidth for transfer. In such a hypotheticalsituation, a rate of sensor data capture serves as the referenceparameter to be the basis for relative weighting of different appetiteforecast parameters instead of measured environmental data, whereby theprocessing system 210 preferentially underweights an appetite forecastmodel based on its access to a lower quantity of data untillow-bandwidth conditions are resolved. Similarly, in anotherhypothetical situation, the image-based second set of sensors may beconfigured to downsample captured images prior to transmission to theprocessing system 210 in response to low-bandwidth issues. In such ahypothetical situation, a qualitative measure of data (such as relativeto an expected baseline) serves as the reference parameter to be thebasis for relative weighting of different appetite forecast parametersinstead of measured environmental data, whereby the processing system210 preferentially underweights an appetite forecast model based on itsaccess to a lower quality of data until low-bandwidth conditions areresolved.

Sensor measurements corresponding to different data sets (e.g., firstand second parameter data sets 108 a, 108 b) may be captured withdiffering spatial granularities. With reference back to system 200, animage-based second set of sensors may be configured such that a singlecamera is oriented to capture underwater imagery at a farming sitecontaining multiple sea cages (not shown) that each hold a differentpopulation of fish 212 in one embodiment. In another embodiment, animage-based second set of sensors may be configured such that a camerais allocated for each of the multiple sea cages. In yet anotherembodiment, an image-based second set of sensors may be configured suchthat multiple cameras are allocated for each of the multiple sea cages(e.g., each of the multiple cameras monitoring a different portionwithin the volume of the sea cage). As will be appreciated, each ofthese different embodiments captures image data at a different spatialgranularity with respect to the amount of area encompassing eachcamera's field of view. Across such embodiments, a resolution of datacapture as it relates to spatial granularity serves as the referenceparameter to be the basis for weighting of appetite forecast parameters.For example, the processing system 210 may preferentially overweight anappetite forecast model based on its access to multiple camera streamscovering multiple points of view.

It will also be appreciated that the granularity concept is applicablenot only to relative weightings between different feeding appetiteforecast models but also to weightings as to which reference parametersshould be more or less influential in the determination of modelweightings. For example, with respect to spatial granularity, consider afirst hypothetical having a first set of environmental conditions suchas previously described with reference to FIG. 3 in which the weather issunny, waters are clean, and waves are choppy. In this firsthypothetical, the environmental conditions are determined based onenvironmental sensors (e.g., using the third sensor system 202 c) thatmeasure and generate environmental data locally at a location proximateto the population of fish 212 and the processing system 210 generatesthe relative weightings of FIG. 3.

Now consider a second hypothetical having a second set of environmentalconditions similar to the first set in which the weather is sunny,waters are clean, and waves are choppy. However, in this secondhypothetical, reference data corresponding to the sunny weatherconditions is measured and generated at a weather sensing station thatis remotely located away from the location of the population of fish212. For example, in some embodiments, the weather data includes remotesensing data from satellite imagery and data, for example with aresolution of approximately 1000 square miles. In other embodiments, theweather data includes remote sensing upper air (e.g., as captured viaradiosondes and the like) data with a resolution of approximately 100square miles. In other embodiments, the weather data includes surfacecoastal station data with higher accuracy but a resolution ofapproximately 10 square miles. Further, in various embodiments, theweather data includes measurements from, for example, in-situ sensorsincluding buoys, gliders, flying drones, and the like with varyingdegrees of accuracy and resolution. As will be appreciated, the varyingspatial granularity at which these weather sensors capture data willaffect the underlying feeding appetite forecast models and the relativeweights assigned to different feeding appetite forecasts. Accordingly,in this second hypothetical, the processing system 210 will give lessweight to the sunny weather conditions (relative to its importance withrespect to the first hypothetical discussed above) due to the lesserspatial granularity of sunny weather being determined by a remoteweather sensor. For example, the processing system 210 may assign asecond weighting factor w₂ of less than 0.6 to the image-based, secondappetite forecast model to account for an increase in uncertainty as towhether the sunny conditions measured by the remote sensor are indeedapplicable to the local micro-climate at which the population of fish212 are location.

Additionally, it will be appreciated that the various non-environmentalconsiderations discussed herein are not limited to differinggranularities at which data is collected and/or analyzed (e.g.,temporal, spatial, qualitative, quantitative, or any othercategorization in which data may be assigned relative coarse-to-fineassignations). In some embodiments, the reference data includes anactual amount of feed given for a prior time period. That is, ratherthan the reference parameter data set 208 c corresponding to measuredenvironmental data, the reference parameter data set 208 c includes datacorresponding to how much feed the population of fish 212 actually atewithin each of one or more time intervals (e.g., days, hours, minutes,or any other block of time) for which an amount of administered feed ismeasured. The one or more time intervals for which model weighting isapplied are approximately equal in length to each other, but the lengthmay vary and may be selected based on various factors includingavailability of data, desired degree of accuracy, and the like.

In various embodiments, assigning of the relative weight factors betweena first weight factor and a second weight factor (e.g., first and secondweighting factors w₁, w₂ of FIG. 3) includes assigning the first weightfactor based on a comparison of a first predicted feed amount providedby the first feeding appetite forecast model relative to the actualamount of feed given for the prior time period. Further, assigning ofthe relative weight factors includes assigning the second weight factorbased on a comparison of a second predicted feed amount provided by thesecond feeding appetite forecast model relative to the actual amount offeed given for the prior time period. For example, in one embodiment,the actual amount of feed given for the prior time period (e.g., priorfull day of feeding) corresponds to data regarding an amount of feedgiven to a population of fish by an experienced feeder. In this manner,the processing system compares a predicted amount of feed associatedwith the first feeding appetite forecast model from the prior day andalso a predicted amount of feed associated with the second feedingappetite forecast model from the prior day relative to the actual amountof feed given. Using this comparison, the processing system may assignrelative weightings to the two or more different appetite forecastmodels based at least in part on their respective capabilities topredict the approximate actions that the experienced feeder would havetaken.

In other embodiments, the reference data includes a calculated amountsuch as a feed table amount of feed calculated to be given for a priortime period. That is, rather than the reference parameter data set 208 ccorresponding to measured environmental data, the reference parameterdata set 208 c includes data corresponding to a feed table forcalculating feed rations (e.g., a daily feed ration). Such feed tablesmay be provided by feed manufacturers to provide feeding recommendationsthat provide approximations based on raw numbers of individual fish tobe fed and total biomass. In various embodiments, assigning of therelative weight factors between a first weight factor and a secondweight factor (e.g., first and second weighting factors w₁, w₂ of FIG.3) includes assigning the first weight factor based on a comparison of afirst predicted feed amount provided by the first feeding appetiteforecast model relative to the feed table amount of feed calculated forthe prior time period. Further, assigning of the relative weight factorsincludes assigning the second weight factor based on a comparison of asecond predicted feed amount provided by the second feeding appetiteforecast model relative to the feed table amount of feed calculated forthe prior time period. Using this comparison, the processing system mayassign relative weightings to the two or more different appetiteforecast models based at least in part on their respective capabilitiesto not deviate significantly from a known metric against which industryis comfortable comparing feeding forecast models against.

In various embodiments, the processing systems described herein use oneor more learned models (not shown) to determine the relative weightingsthat should be assigned to various feeding appetite forecast modelsand/or to reference parameters against which influence the variousfeeding appetite forecast models. Further, additional inputs provided,or the results of feeding according to the forecasted appetite, or both,then are incorporated into the learned model so that the learned modelevolves to facilitate the subsequent performance of similar feedingappetite forecasting. In various embodiments, the learned model includesa system represented by one or more data structures, executableinstructions, or combinations thereof, that is trained and having aninternal representation modified or adapted based in input or experienceduring the training process. One example of the learned model is aneural network. Other implementations include parametricrepresentations, such as coefficients for dynamics models, latent orexplicit embedding into metric spaces for methods like nearestneighbors, or the like.

In some embodiments, the learned model is initialized through asupervised learning process (e.g., a “learning by demonstration”process) so as to obtain a baseline set of knowledge regarding theoperational environment 104, 204 and the performance of at least certainfeeding appetite forecasts by the systems 100, 200. In otherembodiments, the learned model may be initiated at a particularprocessing system (e.g., processing systems 110 and 210) by, forexample, populating the learned model with the knowledge of a learnedmodel of other similar appetite forecast models, forecast modelsoptimized for different locales, or a “default knowledge core”maintained by the processing systems 110, 210 for distribution to eachfeeding appetite forecast as additional sensor systems 102 and/orparameter data sets 108 are integrated into the systems 100, 200 orotherwise become available to storage and processing.

With the relative weightings assigned to their respective feedingappetite forecast models, at block 408 the processing system determinesan aggregated appetite score based on a weighted combination of theappetite forecasts from a plurality of feeding appetite forecast models.In this context, the “weighted combination” and “aggregated appetitescore” can be specified in various ways, depending on the goals andparametric outputs specified by the systems 100, 200. For example, inone embodiment and with reference to FIG. 3, determining the aggregatedappetite score includes normalizing a first model score 302 a of 80(e.g., based on a first scale having one hundred points from 0-100) anda second model score 302 b of 8.5 (e.g., based on a second scale havingten points from 0.0-10.0) to a common unit scale (i.e., the same onehundred point scale) to generate a normalized score for the first andsecond model scores 302 a, 302 b. In particular, the processing system210 normalizes the model scores based on a one hundred point scale togenerate a first normalized model score 304 a of 80 associated with thefirst model score 302 a and a second normalized model score 304 b of 85associated with the second model score 302 b. Subsequently, theprocessing system 210 applies the assigned weighting factors w₁=0.4 andw₂=0.6 to the first normalized model score 304 a and the secondnormalized model score 304 b, respectively, to generate a first weightedmodel score 306 a of 32 and a second weighted model score 306 b of 51.Further, the processing system 210 combines these two weighted modelscores 306 a, 306 b to generate a weighted, aggregated appetite score308 of 83 and thereby integrates data from multi-sensor systems toprovide an appetite forecast.

Those skilled in the art will recognize that the example aggregatedappetite score of FIG. 3 based on a scale having one hundred points from0-100is provided only for illustrative purposes to give a concreteexample of the weighting and score aggregation operations discussedherein. However, any of a variety of unit scales and/or user interfaceschemes may be implemented for representing the aggregated appetitescore. For example, in any of the exemplary systems disclosed here,color coding may be used to indicate categories of any parameter. Forexample, in the display of a user interface, color coding may be used toindicate whether a population of fish is predicted to be starving (e.g.,with the color red), hungry (e.g., with the color yellow), or satiated(e.g., with the color green). Similarly, color coding may be used toindicate whether a feeder should, based on the aggregated appetite, stopfeeding due to reaching satiation (e.g., with the color red), beginmonitoring for signs of satiation (e.g., with the color yellow), orbegin/continue feeding (e.g., with the color green).

Returning now to numerical representations of the aggregated appetitescore, those skilled in the art will recognize that the aggregatedappetite score is not limited to providing a metric associated withhunger levels but may instead (or additionally) prescribe specificactions to be taken. For example, in some embodiments, the processingsystem 110, 210 uses the aggregated appetite score to determine apredicted amount of feed to administer for a particular time period.Such an output may be provided to, for example, automated feedingsystems to eliminate or reduce human intervention as it associates tofeeding activities. Further, the unit interval within a scale is notlimited to being linear and in various embodiments, the unit interval istransformed to have any desired distribution within a scale (e.g., ascale including 100 points from 0 to 100), for example, arctangent,sigmoid, sinusoidal, and the like. In certain distributions, theintensity values increase at a linear rate along the scale, and inothers, at the highest ranges the intensity values increase at more thana linear rate to indicate that it is more difficult to climb in thescale toward the extreme end of the scale. In some embodiments, the rawintensity scores are scaled by fitting a curve to a selected group ofcanonical exercise routines that are predefined to have particularintensity scores.

Thus, the operations of method 400 provides variable and relativeweighting to different feeding appetite predictions from differentfeeding appetite forecast models and combines them into an aggregatedappetite score that is more accurate than would be individually providedby each feeding appetite forecast model by itself. It should be notedthat the method 400 is illustrated as a single instance of relativeweighting between two appetite forecast models based on a singlereference parameter for ease of illustration and description. However,in some embodiments, after a single pass through the operations ofblocks 402-408 is completed for determining an appetite forecast for afirst time period, the operations of blocks 402-408 may be repeated foradditional passes to determine an appetite forecast for a second timeperiod (e.g., such as to provide continually updating forecasts) or adifferent time period interval (e.g., such as to provide more or lessgranular forecasts). In some embodiments, after a single pass throughthe operations of blocks 402-408 is completed for determining anappetite forecast, the operations of blocks 402-408 may be repeated forre-weighting as environmental conditions change, refining of relativeweightings to adjust for additional factors (e.g., consideration of morethan one reference parameter data set), and the like.

Referring now to FIG. 5, illustrated is a diagram showing a system 500implementing a set of underwater sensors and a set of out of watersensors in accordance with some embodiments. In various embodiments, thesystem 500 includes a plurality of sensor systems 502 that are eachconfigured to monitor and generate data associated with the environment504 within which they are placed. As shown, the plurality of sensorsystems 502 includes a first sensor system 502 a positioned below thewater surface 506 and including a first set of one or more sensors. Thefirst set of one or more sensors are configured to monitor theenvironment 504 below the water surface 506 and generate data associatedwith a first feeding parameter. In particular, the first sensor system502 a of FIG. 5 includes one or more hydroacoustic sensors (e.g.,similar to those previously discussed with respect to FIG. 2) configuredto observe fish behavior and capture measurements associated withfeeding parameters related to fish appetite. For example, in variousembodiments, the hydroacoustic sensors are configured to captureacoustic data corresponding to the presence (or absence), abundance,distribution, size, location, and/or behavior of underwater objects(e.g., a population of fish 512 as illustrated in FIG. 5 or feed pellets514). Such acoustic data measurements may therefore measure fishpositions within the water to be used as an approximation of appetite.

The one or more hydroacoustic sensors of the first sensor system 502 aincludes one or more of a passive acoustic sensor and/or an activeacoustic sensor (e.g., an echo sounder and the like). Passive acousticsensors generally listen for remotely generated sounds (e.g., often atspecified frequencies or for purposes of specific analyses, such as fordetecting fish or feed in various aquatic environments) withouttransmitting into the underwater environment 504. In contrast, activeacoustic sensors conventionally include both an acoustic receiver and anacoustic transmitter that transmit pulses of sound (e.g., pings) intothe surrounding environment 504 and then listens for reflections (e.g.,echoes) of the sound pulses. It is noted that as sound waves/pulsestravel through water, it will encounter objects having differingdensities or acoustic properties than the surrounding medium (i.e., theunderwater environment 504) that reflect sound back towards the activesound source(s) utilized in active acoustic systems. For example, soundtravels differently through fish 512 (and other objects in the watersuch as feed pellets 514) than through water (e.g., a fish's air-filledswim bladder has a different density than water). Accordingly,differences in reflected sound waves from active acoustic techniques dueto differing object densities may be accounted for in the detection ofaquatic life and estimation of their individual sizes or total biomass.

In various embodiments, the first sensor system 502 a utilizes activesonar systems in which pulses of sound are generated using a sonarprojector including a signal generator, electro-acoustic transducer orarray, and the like. The active sonar system may further include abeamformer (not shown) to concentrate the sound pulses into an acousticbeam 516 covering a certain search angle 518. In some embodiments, thefirst sensor system 502 a measures distance through water between twosonar transducers or a combination of a hydrophone (e.g., underwateracoustic microphone) and projector (e.g., underwater acoustic speaker).In some embodiments, the first sensor system 502 a includes sonartransducers (not shown) for transmitting and receiving acoustic signals(e.g., pings). To measure distance, one transducer (or projector)transmits an interrogation signal and measures the time between thistransmission and the receipt of a reply signal from the other transducer(or hydrophone). The time difference, scaled by the speed of soundthrough water and divided by two, is the distance between the twoplatforms. This technique, when used with multiple transducers,hydrophones, and/or projectors calculates the relative positions ofobjects in the underwater environment 504.

In other embodiments, the first sensor system 202 a includes an acoustictransducer configured to emit sound pulses into the surrounding watermedium. Upon encountering objects that are of differing densities thanthe surrounding water medium (e.g., the fish 212), those objects reflectback a portion of the sound towards the sound source (i.e., the acoustictransducer). Due to acoustic beam patterns, identical targets atdifferent azimuth angles will return different echo levels. Accordingly,if the beam pattern and angle to a target is known, this directivity maybe compensated for. In various embodiments, split-beam echosoundersdivide transducer faces into multiple quadrants and allow for locationof targets in three dimensions. Similarly, multi-beam sonar projects afan-shaped set of sound beams outward from the first sensor system 202 aand record echoes in each beam, thereby adding extra dimensions relativeto the narrower water column profile given by an echosounder. Multiplepings may thus be combined to give a three-dimensional representation ofobject distribution within the water environment 204.

In some embodiments, the one or more hydroacoustic sensors of the firstsensor system 202 a includes a Doppler system using a combination ofcameras and utilizing the Doppler effect to monitor the appetite ofsalmon in sea pens. The Doppler system is located underwater andincorporates a camera, which is positioned facing upwards towards thewater surface 206. In various embodiments, there is a further cameraintegrated in the transmission device normally positioned on thetop-right of the pen, monitoring the surface of the pen. The sensoritself uses the Doppler effect to differentiate pellets from fish. Thesensor produces an acoustic signal and receives the echo. The sensor istuned to recognize pellets and is capable of transmitting theinformation by radio link to the feed controller over extendeddistances. The user watches the monitor and determines when feedingshould be stopped. Alternatively, a threshold level of waste pellets canbe set by the operator, and the feeder will automatically switch offafter the threshold level is exceeded.

In other embodiments, the one or more hydroacoustic sensors of the firstsensor system 202 a includes an acoustic camera having a microphonearray (or similar transducer array) from which acoustic signals aresimultaneously collected (or collected with known relative time delaysto be able to use phase different between signals at the differentmicrophones or transducers) and processed to form a representation ofthe location of the sound sources. In various embodiments, the acousticcamera also optionally includes an optical camera.

Additionally, as illustrated in FIG. 5, the plurality of sensor systems502 includes a second sensor system 502 b positioned above the watersurface 506 and including a second set of one or more sensors. Thesecond set of one or more sensors are configured to monitor theenvironment 504 proximate to (e.g., at the water surface or evenslightly underwater if the one or more sensors are capable of imaging)and above the water surface 506 and generate data associated with asecond feeding parameter. In particular, the second sensor system 502 bof FIG. 5 includes one or more imaging sensors configured to observefish behavior and capture measurements associated with feedingparameters related to fish appetite. In various embodiments, the imagingsensors are configured to capture image data corresponding to, forexample, the presence (or absence), abundance, distribution, size, andbehavior of objects (e.g., a population of fish 512 as illustrated inFIG. 5). Such image data measurements may therefore be used to identifyfish activity for approximation of appetite. It should be recognizedthat although specific sensors are described below for illustrativepurposes, various imaging sensors may be implemented in the systemsdescribed herein without departing from the scope of this disclosure.

In some embodiments, the imaging sensors of the second sensor system 502b includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the environment proximate the water surface 506, witheach camera capturing a sequence of images (e.g., video frames) of theenvironment 504 and any objects in the environment. In variousembodiments, each camera has a different viewpoint or pose (i.e.,location and orientation) with respect to the environment. Although FIG.5 only shows a single camera for ease of illustration and description,persons of ordinary skill in the art having benefit of the presentdisclosure should appreciate that the second sensor system 502 b caninclude any number of cameras and which may account for parameters suchas each camera's horizontal field of view, vertical field of view, andthe like. Further, persons of ordinary skill in the art having benefitof the present disclosure should appreciate that the second sensorsystem 502 b can include any arrangement of cameras (e.g., cameraspositioned on different planes relative to each other, single-planearrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the second sensor system502 b includes a first camera (or lens) having a particular field ofview 520 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 504 or atleast a portion thereof. For the sake of clarity, only the field of view520 for a single camera is illustrated in FIG. 5. In variousembodiments, the imaging sensors of the second sensor system 502 bincludes at least a second camera (or lens) having a different butoverlapping field of view (not shown) relative to the first camera (orlens). Images from the two cameras therefore form a stereoscopic pairfor providing a stereoscopic view of objects in the overlapping field ofview. Further, it should be recognized that the overlapping field ofview is not restricted to being shared between only two cameras. Forexample, at least a portion of the field of view 520 of the first cameraof the second sensor system 502 b may, in some embodiments, overlap withthe fields of view of two other cameras to form an overlapping field ofview with three different perspectives of the environment 504.

In some embodiments, the imaging sensors of the second sensor system 502b includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 504. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the second sensor system 502 bincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras for ease of descriptionand illustration. However, it should be recognized that the operationsdescribed herein may similarly be implemented with any type of imagingsensor without departing from the scope of this disclosure. For example,in various embodiments, the imaging sensors of the second sensor system502 b may include, but are not limited to, any of a number of types ofoptical cameras (e.g., RGB and infrared), thermal cameras, range- anddistance-finding cameras (e.g., based on acoustics, laser, radar, andthe like), stereo cameras, structured light cameras, ToF cameras,CCD-based cameras, CMOS-based cameras, machine vision systems, lightcurtains, multi- and hyper-spectral cameras, thermal cameras, and thelike.

Additionally, as illustrated in FIG. 5, the plurality of sensor systems502 includes a third sensor system 502 c including a third set of one ormore sensors. The third set of one or more sensors are configured tomonitor the environment 504 and generate data associated with areference parameter. In particular, the third sensor system 502 c ofFIG. 5 includes one or more environmental sensors configured to capturemeasurements associated with the environment 504 within which the system500 is deployed. Although the third sensor system 502 is shown in FIG. 5to be positioned above the water surface 506 to illustrate thatenvironmental sensors are not required to be deployed below the watersurface (e.g., in contrast to the illustration of FIG. 2), those skilledin the art will recognize that one or more of the environmental sensorsof the third sensor system 502 c may be deployed under the watersurface, at the water surface, above the water surface, remote to thelocale at which the fish 512 are located, remote to the processingsystem 510, or any combination of the above without departing from thescope of this disclosure.

As described in further detail below, in various embodiments, theenvironmental sensors of the third sensor system 502 c generateenvironmental data that serves as reference data for implementing thedynamic weighting of various forecasts from a plurality of feedingappetite forecast models. For example, in one embodiment, theenvironmental sensors of the third sensor system 502 c includes anambient light sensor or other photodetector configured to sense orotherwise measure an amount of ambient light present within theenvironment local to the sensor. It should be recognized that althoughFIG. 5 is described in the specific context of an ambient light sensor,the third sensor system 502 c may include any number of and anycombination of various environmental sensors without departing from thescope of this disclosure.

The first sensor system 502 a and the second sensor system 502 b eachgenerate a first feeding parameter data set 508 a and a second feedingparameter data set 508 b, respectively. In the context of FIG. 5, thefirst feeding parameter includes acoustic data. Such acoustic data mayinclude any acoustics-related value or other measurablefactor/characteristic that is representative of at least a portion of adata set that describes the presence (or absence), abundance,distribution, size, and/or behavior of underwater objects (e.g., apopulation of fish 512 as illustrated in FIG. 5). For example, theacoustic data may include acoustic measurements indicative of therelative and/or absolute locations of individual fish of the populationof fish 512 within the environment 504. It should be recognized thatalthough the first feeding parameter has been abstracted and describedhere generally as “acoustic data” for ease of description, those skilledin the art will understand that acoustic data (and therefore the firstfeeding parameter data set 508 a corresponding to the acoustic data) mayinclude, but is not limited to, any of a plurality of acousticsmeasurements, acoustic sensor specifications, operational parameters ofacoustic sensors, and the like.

In the context of FIG. 5, the second feeding parameter includes imagedata. Such image data may include any image-related value or othermeasurable factor/characteristic that is representative of at least aportion of a data set that describes the presence (or absence),abundance, distribution, size, and/or behavior of objects (e.g., apopulation of fish 512 as illustrated in FIG. 5). For example, the imagedata may include camera images capturing measurements representative ofthe relative and/or absolute locations of individual fish of thepopulation of fish 512 within the environment 504. The image data mayalso include camera images capturing measurements representative of thebehavior of individual fish of the population of fish 512. It should berecognized that although the second feeding parameter has beenabstracted and described here generally as “image data” for ease ofdescription, those skilled in the art will understand that image data(and therefore the second feeding parameter data set 508 b correspondingto the image data) may include, but is not limited to, any of aplurality of image frames, extrinsic parameters defining the locationand orientation of the image sensors, intrinsic parameters that allow amapping between camera coordinates and pixel coordinates in an imageframe, camera models, operational parameters of the image sensors (e.g.,shutter speed), depth maps, and the like.

Similarly, in the context of FIG. 5, the reference parameter includesenvironmental data. Such environmental data may include any measurementrepresentative of the environment 504 within which the environmentalsensors are deployed. For example, the environmental data (and thereforethe reference parameter data set 508 c corresponding to theenvironmental data) may include, but is not limited to, any of aplurality of ambient light measurements, water turbidity measurements,water temperature measurements, metocean measurements, satellite weathermeasurements, weather forecasts, air temperature, dissolved oxygen,current direction, current speeds, and the like.

In various embodiments, the processing system 510 receives one or moreof the data sets 508 (e.g., first feeding parameter data set 508 a, thesecond feeding parameter data set 508 b, and the reference parameterdata set 508 c) via, for example, wired-telemetry, wireless-telemetry,or any other communications links for processing. The processing system510 provides the first feeding parameter data set 508 a to a firstfeeding appetite forecast model 522 a. The processing system 510 alsoprovides the second feeding parameter data set 508 b to a second feedingappetite forecast model 522 b different from the first feeding appetiteforecast model 522 a. In various embodiments, the first feeding appetiteforecast model 522 a receives the acoustic data of the first feedingparameter data set 508 a as input and generates a first feeding appetiteforecast 524 a. By way of non-limiting example, in some embodiments, thefirst feeding appetite forecast model 522 a utilizes acoustic datarelated to fish position within the water below the water surface 506 asa proxy for appetite (as appetite is a value which cannot be directlymeasured and must be inferred) in generating the first feeding appetiteforecast 524 a. In various embodiments, the first feeding appetiteforecast 524 a is a description of a possible hunger level expected tobe exhibited by the population of fish 212 within the water for a futuretime period (i.e., a feeding appetite prediction).

The processing system 510 also provides the second feeding parameterdata set 508 b to a second feeding appetite forecast model 522 b. Invarious embodiments, the second feeding appetite forecast model 522 breceives the image data of the second feeding parameter data set 508 bas input and generates a second feeding appetite forecast 524 b. By wayof non-limiting example, in some embodiments, the second feedingappetite forecast model 522 b utilizes image data related to fishactivity at the water surface 506 as an appetite proxy for generatingthe second feeding appetite forecast 224 b. For example, the image datacaptured by the second set of sensors 502 b may be analyzed to quantifyor otherwise determine a level of surface level activity exhibited bythe fish 512 (e.g., resulting from fish jumping out of the water asillustrated, rolling along the water surface 506, splashes at the watersurface 506 caused by fish jumping, and the like) as an appetite proxyfor generating the second feeding appetite forecast 224 b. In variousembodiments, the second feeding appetite forecast 524 b is a descriptionof a possible hunger level to be exhibited by the population of fish 512within the water for a future time period (i.e., a feeding appetiteprediction).

Subsequently, such as previously discussed in more detail with referenceto FIGS. 3 and 4, the processing system 510 adaptively weights the firstfeeding appetite forecast 524 a of the first feeding appetite forecastmodel 522 a with a first weighting factor relative to a second weightingfactor for a second feeding appetite forecast 524 b of the secondfeeding appetite forecast model 522 b in order to determine anaggregated appetite score 526 based on a combination of the firstfeeding appetite forecast model 522 a using the first weight factor andthe second feeding appetite forecast model 522 b using the second weightfactor.

It should be recognized that although appetite forecasting andaggregated appetite scoring has been primarily discussed here in thecontext of sensors capturing different data types, any combination ofsensors including multiple sensors capturing similar data may beemployed for any of a variety of possible configurations withoutdeparting from the scope of this disclosure. For example, and nowreferring to FIG. 6, illustrated is a diagram showing a system 600implementing two sets of image-based sensors in accordance with someembodiments. In various embodiments, the system 600 includes a pluralityof sensor systems 602 that are each configured to monitor and generatedata associated with the environment 604 within which they are placed.

As shown, the plurality of sensor systems 602 includes a first sensorsystem 602 a positioned below the water surface 606 and including afirst set of one or more sensors. The first set of one or more sensorsare configured to monitor the environment 604 below the water surface606 and generate data associated with a first feeding parameter. Inparticular, the first sensor system 602 a of FIG. 6 includes one or moreimaging sensors configured to observe fish behavior and capturemeasurements associated with feeding parameters related to fishappetite. In various embodiments, the imaging sensors are configured tocapture image data corresponding to, for example, the presence (orabsence), abundance, distribution, size, and behavior of underwaterobjects (e.g., a population of fish 612 as illustrated in FIG. 6). Suchimage data measurements may therefore be used to identify fish positionswithin the water for approximation of appetite. It should be recognizedthat although specific sensors are described below for illustrativepurposes, various imaging sensors may be implemented in the systemsdescribed herein without departing from the scope of this disclosure.

In some embodiments, the imaging sensors of the first sensor system 602a includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the surrounding environment 604 below the water surface606, with each camera capturing a sequence of images (e.g., videoframes) of the environment 604 and any objects in the environment. Invarious embodiments, each camera has a different viewpoint or pose(i.e., location and orientation) with respect to the environment.Although FIG. 6 only shows a single camera for ease of illustration anddescription, persons of ordinary skill in the art having benefit of thepresent disclosure should appreciate that the first sensor system 602 acan include any number of cameras and which may account for parameterssuch as each camera's horizontal field of view, vertical field of view,and the like. Further, persons of ordinary skill in the art havingbenefit of the present disclosure should appreciate that the firstsensor system 602 b can include any arrangement of cameras (e.g.,cameras positioned on different planes relative to each other,single-plane arrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the first sensor system602 a includes a first camera (or lens) having a particular field ofview 618 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 604 or atleast a portion thereof. For the sake of clarity, only the field of view618 for a single camera is illustrated in FIG. 6. In variousembodiments, the imaging sensors of the first sensor system 602 aincludes at least a second camera having a different but overlappingfield of view (not shown) relative to the first camera (or lens). Imagesfrom the two cameras therefore form a stereoscopic pair for providing astereoscopic view of objects in the overlapping field of view. Further,it should be recognized that the overlapping field of view is notrestricted to being shared between only two cameras. For example, atleast a portion of the field of view 618 of the first camera of thefirst sensor system 602 a may, in some embodiments, overlap with thefields of view of two other cameras to form an overlapping field of viewwith three different perspectives of the environment 604.

In some embodiments, the imaging sensors of the first sensor system 602a includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 604. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the first sensor system 602 aincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras for ease of descriptionand illustration. However, it should be recognized that the operationsdescribed herein may similarly be implemented with any type of imagingsensor without departing from the scope of this disclosure. For example,in various embodiments, the imaging sensors of the first sensor system602 a may include, but are not limited to, any of a number of types ofoptical cameras (e.g., RGB and infrared), thermal cameras, range- anddistance-finding cameras (e.g., based on acoustics, laser, radar, andthe like), stereo cameras, structured light cameras, ToF cameras,CCD-based cameras, CMOS-based cameras, machine vision systems, lightcurtains, multi- and hyper-spectral cameras, thermal cameras, and thelike.

Additionally, as illustrated in FIG. 6, the plurality of sensor systems602 includes a second sensor system 602 b positioned above the watersurface 606 and including a second set of one or more sensors. Thesecond set of one or more sensors are configured to monitor theenvironment 604 proximate to (e.g., at the water surface or evenslightly underwater if the one or more sensors are capable of imaging)and above the water surface 606 and generate data associated with asecond feeding parameter. In particular, the second sensor system 602 bof FIG. 6 includes one or more imaging sensors configured to observefish behavior and capture measurements associated with feedingparameters related to fish appetite. In various embodiments, the imagingsensors are configured to capture image data corresponding to, forexample, the presence (or absence), abundance, distribution, size, andbehavior of objects (e.g., a population of fish 612 as illustrated inFIG. 6). Such image data measurements may therefore be used to identifyfish activity for approximation of appetite. It should be recognizedthat although specific sensors are described below for illustrativepurposes, various imaging sensors may be implemented in the systemsdescribed herein without departing from the scope of this disclosure.

In some embodiments, the imaging sensors of the second sensor system 602b includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the environment proximate the water surface 606, witheach camera capturing a sequence of images (e.g., video frames) of theenvironment 604 and any objects in the environment. In variousembodiments, each camera has a different viewpoint or pose (i.e.,location and orientation) with respect to the environment. Although FIG.6 only shows a single camera for ease of illustration and description,persons of ordinary skill in the art having benefit of the presentdisclosure should appreciate that the second sensor system 602 b caninclude any number of cameras and which may account for parameters suchas each camera's horizontal field of view, vertical field of view, andthe like. Further, persons of ordinary skill in the art having benefitof the present disclosure should appreciate that the second sensorsystem 602 b can include any arrangement of cameras (e.g., cameraspositioned on different planes relative to each other, single-planearrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the second sensor system602 b includes a first camera (or lens) having a particular field ofview 620 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 604 or atleast a portion thereof. For the sake of clarity, only the field of view620 for a single camera is illustrated in FIG. 6. In variousembodiments, the imaging sensors of the second sensor system 602 bincludes at least a second camera (or lens) having a different butoverlapping field of view (not shown) relative to the first camera (orlens). Images from the two cameras therefore form a stereoscopic pairfor providing a stereoscopic view of objects in the overlapping field ofview. Further, it should be recognized that the overlapping field ofview is not restricted to being shared between only two cameras. Forexample, at least a portion of the field of view 620 of the first cameraof the second sensor system 602 b may, in some embodiments, overlap withthe fields of view of two other cameras to form an overlapping field ofview with three different perspectives of the environment 604.

In some embodiments, the imaging sensors of the second sensor system 602b includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 604. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the second sensor system 602 bincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras for ease of descriptionand illustration. However, it should be recognized that the operationsdescribed herein may similarly be implemented with any type of imagingsensor without departing from the scope of this disclosure. For example,in various embodiments, the imaging sensors of the second sensor system602 b may include, but are not limited to, any of a number of types ofoptical cameras (e.g., RGB and infrared), thermal cameras, range- anddistance-finding cameras (e.g., based on acoustics, laser, radar, andthe like), stereo cameras, structured light cameras, ToF cameras,CCD-based cameras, CMOS-based cameras, machine vision systems, lightcurtains, multi- and hyper-spectral cameras, thermal cameras, and thelike.

Additionally, as illustrated in FIG. 6, the plurality of sensor systems602 includes a third sensor system 602 c including a third set of one ormore sensors. As described in further detail below, in variousembodiments, the environmental sensors of the third sensor system 602 cgenerate environmental data that serves as reference data forimplementing the dynamic weighting of various forecasts from a pluralityof feeding appetite forecast models. For example, in one embodiment, theenvironmental sensors of the third sensor system 602 c includes anambient light sensor or other photodetector configured to sense orotherwise measure an amount of ambient light present within theenvironment local to the sensor. It should be recognized that althoughFIG. 6 is described in the specific context of an ambient light sensor,the third sensor system 602 c may include any number of and anycombination of various environmental sensors without departing from thescope of this disclosure.

Further, the plurality of sensor systems 602 includes a fourth sensorsystem 602 d including a fourth set of one or more sensors. The fourthset of one or more sensors are configured to monitor the environment 604below the water surface 606 and generate data associated with areference parameter. As described in further detail below, in variousembodiments, the environmental sensors of the fourth sensor system 602 dgenerate environmental data that serves as reference data forimplementing the dynamic weighting of various forecasts from a pluralityof feeding appetite forecast models. For example, in one embodiment, theenvironmental sensors of the fourth sensor system 602 d includes aturbidity sensor configured to measure an amount of light scattered bysuspended solids in the water. In general, the more total suspendedparticulates or solids in water, the higher the turbidity and thereforemurkier the water appears. It should be recognized that although FIG. 6is described in the specific context of a turbidity sensor, the fourthsensor system 602 d may include any number of and any combination ofvarious environmental sensors without departing from the scope of thisdisclosure.

The first sensor system 602 a and the second sensor system 602 b eachgenerate a first feeding parameter data set 608 a and a second feedingparameter data set 608 b, respectively. In the context of FIG. 6, thefirst feeding parameter includes image data captured from below thewater surface 606 and the second feeding parameter includes image datacaptured with respect to the water surface 606 or from above the watersurface 606. Such image data may include any image-related value orother measurable factor/characteristic that is representative of atleast a portion of a data set that describes the presence (or absence),abundance, distribution, size, and/or behavior of objects (e.g., apopulation of fish 612 as illustrated in FIG. 6).

For example, in various embodiments, the image data of the first andsecond feeding parameter data sets 608 a, 608 b includes camera imagescapturing measurements representative of the relative and/or absolutelocations of individual fish of the population of fish 612 within theenvironment 604. The image data may also include camera images capturingmeasurements representative of the behavior of individual fish of thepopulation of fish 612. It should be recognized that although the firstfeeding parameter and the second feeding parameter has been abstractedand described here generally as “image data” for ease of description,those skilled in the art will understand that image data (and thereforethe first feeding parameter data set 608 a and the second feedingparameter data set 608 b corresponding to the image data) may include,but is not limited to, any of a plurality of image frames, extrinsicparameters defining the location and orientation of the image sensors,intrinsic parameters that allow a mapping between camera coordinates andpixel coordinates in an image frame, camera models, operationalparameters of the image sensors (e.g., shutter speed), depth maps, andthe like.

Similarly, in the context of FIG. 6, the reference parameter includesenvironmental data. Such environmental data may include any measurementrepresentative of the environment 604 within which the environmentalsensors are deployed. For example, the environmental data (and thereforethe first reference parameter data set 608 c and the second referenceparameter data set 608 d corresponding to the environmental data) mayinclude, but is not limited to, any of a plurality of ambient lightmeasurements, water turbidity measurements, water temperaturemeasurements, metocean measurements, satellite weather measurements,weather forecasts, air temperature, dissolved oxygen, current direction,current speeds, and the like.

In various embodiments, the processing system 610 receives one or moreof the data sets 608 (e.g., first feeding parameter data set 608 a, thesecond feeding parameter data set 608 b, the first reference parameterdata set 608 c, and the second reference parameter data set 608 d) via,for example, wired-telemetry, wireless-telemetry, or any othercommunications links for processing. The processing system 610 providesthe first feeding parameter data set 608 a to a first feeding appetiteforecast model 622 a. The processing system 610 also provides the secondfeeding parameter data set 608 b to a second feeding appetite forecastmodel 622 b different from the first feeding appetite forecast model 622a. In various embodiments, the first feeding appetite forecast model 622a receives the image data of the first feeding parameter data set 608 aas input and generates a first feeding appetite forecast 624 a. By wayof non-limiting example, in some embodiments, the first feeding appetiteforecast model 622 a utilizes image data related to fish position withinthe water below the water surface 606 as a proxy for appetite (asappetite is a value which cannot be directly measured and must beinferred) in generating the first feeding appetite forecast 624 a. Invarious embodiments, the first feeding appetite forecast 624 a is adescription of a possible hunger level expected to be exhibited by thepopulation of fish 612 within the water for a future time period (i.e.,a feeding appetite prediction).

The processing system 610 also provides the second feeding parameterdata set 608 b to a second feeding appetite forecast model 622 b. Invarious embodiments, the second feeding appetite forecast model 622 breceives the image data of the second feeding parameter data set 608 bas input and generates a second feeding appetite forecast 624 b. By wayof non-limiting example, in some embodiments, the second feedingappetite forecast model 622 b utilizes image data related to fishactivity at the water surface 606 as an appetite proxy for generatingthe second feeding appetite forecast 624 b. For example, the image datacaptured by the second set of sensors 602 b may be analyzed to quantifyor otherwise determine a level of surface level activity exhibited bythe fish 612 (e.g., resulting from fish jumping out of the water asillustrated, rolling along the water surface 606, splashes at the watersurface 606 c caused by jumping, and the like) as an appetite proxy forgenerating the second feeding appetite forecast 224 b. In variousembodiments, the second feeding appetite forecast 624 b is a descriptionof a possible hunger level to be exhibited by the population of fish 612within the water for a future time period (i.e., a feeding appetiteprediction).

Subsequently, such as previously discussed in more detail with referenceto FIGS. 3 and 4, the processing system 610 adaptively weights the firstfeeding appetite forecast 624 a of the first feeding appetite forecastmodel 622 a with a first weighting factor relative to a second weightingfactor for a second feeding appetite forecast 624 b of the secondfeeding appetite forecast model 622 b in order to determine anaggregated appetite score 626 based on a combination of the firstfeeding appetite forecast model 622 a using the first weight factor andthe second feeding appetite forecast model 622 b using the second weightfactor. For example, in one embodiment, the processing system 610 maypreferentially weight the second feeding appetite forecast model 622 b(i.e., model based on surface camera images) relative to the firstfeeding appetite forecast model 622 a (i.e., model based on sub-surfacecamera images) when environmental conditions are indicated by thereference sensors 602 c, 602 d to include turbid waters but sunnyweather at noon. Similarly, the processing system 610 may preferentiallyunderweight the second feeding appetite forecast model 622 b (i.e.,model based on surface camera images) relative to the first feedingappetite forecast model 622 a (i.e., model based on sub-surface cameraimages) when environmental conditions are indicated by the referencesensors 602 c, 602 d to include clear waters but foggy weatherconditions such that the surface cameras of the second sensor system 602b will be less reliable.

FIG. 7 is a block diagram illustrating a system 700 configured toprovide a feeding appetite forecast in accordance with some embodiments.In some embodiments, the system 700 includes one or more computingplatforms 702. The computing platform(s) 702 may be configured tocommunicate with one or more remote platforms 704 according to aclient/server architecture, a peer-to-peer architecture, and/or otherarchitectures via a network 724. Remote platform(s) 704 may beconfigured to communicate with other remote platforms via computingplatform(s) 702 and/or according to a client/server architecture, apeer-to-peer architecture, and/or other architectures via the network724. Users may access system 700 via remote platform(s) 704. A givenremote platform 704 may include one or more processors configured toexecute computer program modules. The computer program modules may beconfigured to enable an expert or user associated with the given remoteplatform 704 to interface with system 700 and/or one or more externalresource(s) 706, and/or provide other functionality attributed herein toremote platform(s) 704. By way of non-limiting example, a given remoteplatform 704 and/or a given computing platform 702 may include one ormore of a server, a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

In some implementations, the computing platform(s) 702, remoteplatform(s) 704, and/or one or more external resource(s) 706 may beoperatively linked via one or more electronic communication links. Forexample, such electronic communication links may be established, atleast in part, via a network 724 such as the Internet and/or othernetworks. It will be appreciated that this is not intended to belimiting, and that the scope of this disclosure includes implementationsin which computing platform(s) 702, remote platform(s) 704, and/or oneor more external resource(s) 706 may be operatively linked via someother communication media. External resource(s) 706 may include sourcesof information outside of system 700, external entities participatingwith system 700, and/or other resources. In some implementations, someor all of the functionality attributed herein to external resources 706may be provided by resources included in system 700.

In various embodiments, the computing platform(s) 702 are configured bymachine-readable instructions 708 including one or more instructionmodules. In some embodiments, the instruction modules include computerprogram modules for implementing the various operations discussed herein(such as the operations previously discussed with respect to FIG. 4).For purposes of reference, the instruction modules include one or moreof a first feeding parameter module 710, a second feeding parametermodule 712, a first feeding appetite forecast module 714, a secondfeeding appetite forecast module 716, a reference parameter module 718,a dynamic weighting module 720, and an aggregated appetite score module722. Each of these modules may be implemented as one or more separatesoftware programs, or one or more of these modules may be implemented inthe same software program or set of software programs. Moreover, whilereferenced as separate modules based on their overall functionality, itwill be appreciated that the functionality ascribed to any given modelmay be distributed over more than one software program. For example, onesoftware program may handle a subset of the functionality of the firstfeeding parameter module 710 while another software program handlesanother subset of the functionality of the first feeding parametermodule 710 and the functionality of the first feeding appetite forecastmodule 714.

In various embodiments, the first feeding parameter module 710 generallyrepresents executable instructions configured to receive a first feedingparameter data set associated with a first feeding parameter. Withreference to FIGS. 1-6 and 8, in various embodiments, the first feedingparameter module 710 receives sensor data including the first feedingparameter data set via a wireless or wired communications link forstorage, further processing, and/or distribution to other modules of thesystem 700. For example, in the context of FIGS. 2 and 5, the sensorsystems 202, 502 communicate at least the first parameter data sets 208a, 508 a including acoustic data corresponding to the presence (orabsence), abundance, distribution, size, and behavior of underwaterobjects (e.g., a population of fish and feed). In the context of FIG. 6,the sensor system 602 communicates at least a first parameter data set608 a including under water image data corresponding to the presence (orabsence), abundance, distribution, size, and behavior of underwaterobjects (e.g., a population of fish and feed). In various embodiments,such first parameter data sets may be processed by the first feedingparameter module 710 to format or package the data set for use by, forexample, feeding appetite forecast models.

In various embodiments, the second feeding parameter module 712generally represents executable instructions configured to receive asecond feeding parameter data set associated with a second feedingparameter. With reference to FIGS. 1-6 and 8, in various embodiments,the second feeding parameter module 712 receives sensor data includingthe second feeding parameter data set via a wireless or wiredcommunications link for storage, further processing, and/or distributionto other modules of the system 700. For example, in the context of FIG.2, the sensor system 202 communicates at least the second parameter dataset 208 b including under water image data corresponding to for example,the presence (or absence), abundance, distribution, size, and behaviorof underwater objects. In the context of FIGS. 5 and 6, the sensorsystems 502, 602 communicate at least the second parameter data sets 508b, 608 b including image data from the water surface or above the watersurface corresponding to for example, the presence (or absence),abundance, distribution, size, and behavior of objects (e.g., apopulation of fish 512 as illustrated in FIG. 5).

In various embodiments, the first feeding appetite forecast module 714generally represents executable instructions configured to receive thefirst feeding parameter data set from the first feeding parameter module710 and generate a feeding appetite forecast. With reference to FIGS.1-6 and 8, in various embodiments, the first feeding appetite forecastmodule 714 receives one or more data sets embodying parameters relatedto appetite, including factors that directly influence appetite, factorsthat may influence the accuracy of feeding appetite models, and thelike. For example, in the context of FIGS. 2 and 5, the first feedingparameter module 710 receives at least the first parameter data sets 208a, 508 a including acoustic data corresponding to the presence (orabsence), abundance, distribution, size, and behavior of underwaterobjects (e.g., a population of fish and feed). In the context of FIG. 6,the first feeding parameter module 710 receives at least a firstparameter data set 608 a including under water image data correspondingto the presence (or absence), abundance, distribution, size, andbehavior of underwater objects (e.g., a population of fish and feed). Invarious embodiments, the first feeding appetite forecast module 714utilizes one or more learned models (not shown) to generate a firstfeeding appetite forecast representing a possible hunger level that isto be expected for a future time period (i.e., a feeding appetiteprediction), as it is influenced by the feeding parameters of the firstparameter data set.

In various embodiments, additional inputs such as the results of feedingaccording to the forecasted appetite may be incorporated into thelearned model so that the learned model evolves to facilitate thesubsequent performance of similar feeding appetite forecasting. Invarious embodiments, the learned model includes a system represented byone or more data structures, executable instructions, or combinationsthereof, that is trained and having an internal representation modifiedor adapted based in input or experience during the training process. Oneexample of the learned model is a neural network. Other implementationsinclude parametric representations, such as coefficients for dynamicsmodels, latent or explicit embedding into metric spaces for methods likenearest neighbors, or the like.

In some embodiments, the learned model of the first feeding appetiteforecast module 714 is initialized through a supervised learning processso as to obtain a baseline set of knowledge regarding the operationalenvironment and the performance of at least certain feeding appetiteforecasts by the first feeding appetite forecast module 714. In otherembodiments, the learned model may be initiated at a particularprocessing system (e.g., computing platform 702) by, for example,populating the learned model with the knowledge of a learned model ofother similar appetite forecast models, forecast models optimized fordifferent locales, or a “default knowledge core” maintained by thecomputing platform 702 for distribution to each feeding appetiteforecast as additional sensor systems and/or parameter data sets areintegrated into the systems or otherwise become available to storage andprocessing.

In various embodiments, the second feeding appetite forecast module 716generally represents executable instructions configured to receive thesecond feeding parameter data set from the second feeding parametermodule 712 and generate a feeding appetite forecast. With reference toFIGS. 1-6 and 8, in various embodiments, the second feeding appetiteforecast module 716 receives one or more data sets embodying parametersrelated to appetite, including factors that directly influence appetite,factors that may influence the accuracy of feeding appetite models, andthe like. For example, in the context of FIG. 2, the second feedingappetite forecast module 716 receives at least the second parameter dataset 208 b including under water image data corresponding to for example,the presence (or absence), abundance, distribution, size, and behaviorof underwater objects. In the context of FIGS. 5 and 6, the secondfeeding appetite forecast module 716 receives at least the secondparameter data sets 508 b, 608 b including image data from the watersurface or above the water surface corresponding to for example, thepresence (or absence), abundance, distribution, size, and behavior ofobjects (e.g., a population of fish 512 as illustrated in FIG. 5). Invarious embodiments, the second feeding appetite forecast module 716utilizes one or more learned models (not shown) to generate a secondfeeding appetite forecast representing a possible hunger level that isto be expected for a future time period (i.e., a feeding appetiteprediction), as it is influenced by the feeding parameters of the secondparameter data set.

In various embodiments, additional inputs such as the results of feedingaccording to the forecasted appetite may be incorporated into thelearned model so that the learned model evolves to facilitate thesubsequent performance of similar feeding appetite forecasting. Invarious embodiments, the learned model includes a system represented byone or more data structures, executable instructions, or combinationsthereof, that is trained and having an internal representation modifiedor adapted based in input or experience during the training process. Oneexample of the learned model is a neural network. Other implementationsinclude parametric representations, such as coefficients for dynamicsmodels, latent or explicit embedding into metric spaces for methods likenearest neighbors, or the like.

In some embodiments, the learned model of the second feeding appetiteforecast module 716 is initialized through a supervised learning processso as to obtain a baseline set of knowledge regarding the operationalenvironment and the performance of at least certain feeding appetiteforecasts by the second feeding appetite forecast module 716. In otherembodiments, the learned model may be initiated at a particularprocessing system (e.g., computing platform 702) by, for example,populating the learned model with the knowledge of a learned model ofother similar appetite forecast models, forecast models optimized fordifferent locales, or a “default knowledge core” maintained by thecomputing platform 702 for distribution to each feeding appetiteforecast as additional sensor systems and/or parameter data sets areintegrated into the systems or otherwise become available to storage andprocessing.

In various embodiments, the reference parameter module 718 generallyrepresents executable instructions configured to receive one or morefeeding parameter data set associated with a feeding parameter. Withreference to FIGS. 1-6 and 8, in various embodiments, the referenceparameter module 718 receives sensor data including at least onereference parameter data set via a wireless or wired communications linkfor storage, further processing, and/or distribution to other modules ofthe system 700. For example, in the context of FIGS. 2 and 6, the sensorsystems 202, 602 communicate at least the reference parameter data sets208 c, 608 d including water turbidity measurements to the processingsystems 210, 610, respectively. In the context of FIGS. 5 and 6, thesensor systems 502, 602 communicate at least the reference parameterdata sets 508 c, 608 c including ambient light measurements to theprocessing systems 510, 610, respectively.

In various embodiments, the dynamic weighting module 720 generallyrepresents executable instructions configured to adaptively weight afirst feeding appetite forecast (such as the forecast generated by firstfeeding appetite forecast module 714) relative to a second feedingappetite forecast (such as the forecast generated by second feedingappetite forecast module 716). With reference to FIGS. 1-6 and 8, invarious embodiments, the dynamic weighting module 720 receives referencedata such as the reference parameter data sets from the referenceparameter module 718 and compares, for example, environmental conditionsas represented by the reference parameter data sets to determine therelative accuracy amongst a plurality of appetite forecasting modelsunder such environmental conditions. For example, in the context ofFIGS. 2 and 3, the processing system 210 assigns a relative weightingwith the first weighting factor w₁ of 0.4 for the first feeding appetiteforecast model (based on acoustic data) and the second weighting factorw₂ of 0.6 for the second feeding appetite forecast model (based on imagedata) due to a first set of environmental conditions (e.g., usingenvironmental data from the environmental sensors to measure conditionsfor a current time or to forecast for a future time period) in which theweather is sunny, waters are clean, but waves are choppy. However, theprocessing system 210 assigns a relative weighting with the firstweighting factor w₁ of 0.9 for the first feeding appetite forecast model(based on acoustic data) and the second weighting factor w₂ of 0.1 forthe second feeding appetite forecast model (based on image data) due tothe second set of environmental conditions and discounts theimage-based, second feeding appetite forecast model that is expected tobe less accurate in murky waters.

In various embodiments, the aggregated appetite score module 722generally represents executable instructions configured to determine anaggregated appetite score based on a combination of the first feedingappetite forecast model using the first weight factor and the secondfeeding appetite forecast model using the second weight factor. Withreference to FIGS. 1-6 and 8, in various embodiments, the aggregatedappetite score module 722 receives at least the first appetite forecastgenerated by the first feeding appetite forecast module 714, the secondappetite forecast generated by the second feeding appetite forecastmodule 716, and the weighting factors assigned by the dynamic weightingmodule 720. In some embodiments, such as discussed in the context ofFIGS. 2, 3 and 8, the aggregated appetite score module 722 normalizes aplurality of appetite forecasts (e.g., feeding appetite forecasts 224 a,224 b as represented by model scores 302 a-312N in FIGS. 2-3) and/orappetite-related descriptors (e.g., appetite descriptors 802 a-802 e ofFIG. 8) into a common unit scale.

Subsequently, the aggregated appetite score module 722 applies, in thecontext of FIGS. 2-3, the assigned weighting factors w₁, w₂ to the firstnormalized model score 304 a and the second normalized model score 304b, respectively, to generate a first weighted model score 306 a of 32and a second weighted model score 306 b of 51. Further, the aggregatedappetite score module 722 combines these two weighted model scores 306a, 306 b to generate a weighted, aggregated appetite score 308 of 83 andthereby integrates data from multi-sensor systems to provide an appetiteforecast. In some embodiments, such as in the context of FIG. 8, theaggregated appetite score module 722 also generates a feedinginstruction signal 810 based on the aggregated appetite score thatinstructs a user and/or automated feeding system regarding specificactions to be taken in accordance to the feeding appetite prediction.

The system 700 also includes an electronic storage 726 includingnon-transitory storage media that electronically stores information. Theelectronic storage media of electronic storage 726 may include one orboth of system storage that is provided integrally (i.e., substantiallynon-removable) with computing platform(s) 702 and/or removable storagethat is removably connectable to computing platform(s) 702 via, forexample, a port (e.g., a USB port, a firewire port, etc.) or a drive(e.g., a disk drive, etc.). Electronic storage 726 may include one ormore of optically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage726 may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). Electronic storage 726 may store software algorithms,information determined by processor(s) 728, information received fromcomputing platform(s) 702, information received from remote platform(s)704, and/or other information that enables computing platform(s) 702 tofunction as described herein.

Processor(s) 728 may be configured to provide information processingcapabilities in computing platform(s) 702. As such, processor(s) 728 mayinclude one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Althoughprocessor(s) 728 is shown in FIG. 7 as a single entity, this is forillustrative purposes only. In some implementations, processor(s) 728may include a plurality of processing units. These processing units maybe physically located within the same device, or processor(s) 728 mayrepresent processing functionality of a plurality of devices operatingin coordination. Processor(s) 728 may be configured to execute modules710, 712, 714, 716, 718, 720, and/or 722, and/or other modules.Processor(s) 728 may be configured to execute modules 710, 712, 714,716, 718, 720, and/or 722, and/or other modules by software; hardware;firmware; some combination of software, hardware, and/or firmware;and/or other mechanisms for configuring processing capabilities onprocessor(s) 728. As used herein, the term “module” may refer to anycomponent or set of components that perform the functionality attributedto the module. This may include one or more physical processors duringexecution of processor readable instructions, the processor readableinstructions, circuitry, hardware, storage media, or any othercomponents.

It should be appreciated that although modules 710, 712, 714, 716, 718,720, and/or 722 are illustrated in FIG. 7 as being implemented within asingle processing unit, in implementations in which processor(s) 728includes multiple processing units, one or more of modules 710, 712,714, 716, 718, 720, and/or 722 may be implemented remotely from theother modules. The description of the functionality provided by thedifferent modules 710, 712, 714, 716, 718, 720, and/or 722 describedbelow is for illustrative purposes, and is not intended to be limiting,as any of modules 710, 712, 714, 716, 718, 720, and/or 722 may providemore or less functionality than is described. For example, one or moreof modules 710, 712, 714, 716, 718, 720, and/or 722 may be eliminated,and some or all of its functionality may be provided by other ones ofmodules 710, 712, 714, 716, 718, 720, and/or 722. As another example,processor(s) 728 may be configured to execute one or more additionalmodules that may perform some or all of the functionality attributedbelow to one of modules 710, 712, 714, 716, 718, 720, and/or 722.

Referring now to FIG. 8, illustrated is another example diagram 800 ofadaptive weighting of feeding appetite forecasts in accordance with someembodiments. As illustrated, a plurality of feeding appetite forecasts802 (which may be model-based outputs or otherwise) include a firstfeeding appetite descriptor 802 a, a second feeding appetite descriptor802 b, a third feeding appetite descriptor 802 c, a fourth feedingappetite descriptor 802 d, and a fifth feeding appetite descriptor 802e.

The first feeding appetite descriptor 802 a, as represented by thenumber 2, corresponds to a quantification of a number of feed pelletsthat has been administered to a population of fish but has fallen pastthe fish biomass or otherwise gone uneaten. In various embodiments, thefirst feeding appetite descriptor 802 a may be determined based on, forexample, data measured by any of the sensor systems discussed herein,such as acoustics-data based sensor system 202 a of FIG. 2 or byhuman-vision-perception from a video image stream captured by theimage-data based sensor system 202 b of FIG. 2. The low raw number ofuneaten feed pellets of the first feeding appetite descriptor 802 a isindicative of a relatively high level of hunger, as the majority ofadministered feed is being eaten. In contrast, a high raw number ofuneaten feed pellets for the first feeding appetite descriptor 802 a maybe indicative that the fish have reached satiation and have thereforestopped eating. Similarly, a low but gradually increasing number ofuneaten pellets may be indicative that the fish are beginning to reachsatiation.

The second feeding appetite descriptor 802 b, as represented by thenumber 9, corresponds to a human-provided value on an integer scale of0-10. In various embodiments, the second feeding appetite descriptor 802b may be determined via human visual perception and based on feederpersonal experience. For example, in some embodiments, the numericalvalue 9 may represent that the feeder is able to visually identify anumber of fish swimming near the water surface and/or actively jumpingout of the water, behaviors which are indicative of increased hungerlevels. The third feeding appetite descriptor 802 c, as represented bythe color green, corresponds to a color-coded descriptor of hunger in ared, yellow, and green coloring scheme in which green is indicative ofincreased hunger levels.

The fourth feeding appetite descriptor 802 d, as represented by thepercentage value 80%, corresponds to a percentage quantification oftotal biomass positioned at a location within the water that isindicative of hunger. As a hypothetical example, in some embodiments, apredetermined threshold is set such that biomass being located within anupper (i.e., closer to water surface) 1/3 of the volumetric enclosurewithin which the fish are located is indicative of increased hungerlevels. The fifth feeding appetite descriptor 802 e, as represented bythe value 0.4, corresponds to feeding rate instructions as it relates topellets per fish individual per minute (PPFPM). In various embodiments,this 0.4 PPFPM value is indicative of a level of appetite at which fishare hungry but not ravenous such that increasing the feed rate anyfurther would result in waste of feed pellets.

As is evident, each of the plurality of feeding appetite forecasts 802is a descriptor that is related in appetite in some manner. However, itwill be appreciated that the descriptors and values associated with theplurality of feeding appetite forecasts 802 do not share a commonbaseline for comparison. Accordingly, in various embodiments, thesystems described herein (e.g., systems 100-700 of FIGS. 1-7) normalizeseach of the plurality of feeding appetite forecasts 802 based on acomparison to a corresponding baseline or predetermined threshold,respectively, to generate a normalized model score 804. That is, thesystems are configured to normalize each of the feeding appetiteforecasts to a common score scale based on their respective comparisonsrelative to some established parameter, threshold, expected baseline,and the like on which the individual appetite descriptors are based.

For example, with respect to the first feeding appetite forecast, thesystems described herein normalize the first appetite descriptor 802 abased on a comparison to historical feed pellet counts with respect tobiomass location to generate a first normalized model score 804 a of 75based on an example one hundred point scale. Similarly, with respect tothe second feeding appetite forecast, the systems normalize the secondappetite descriptor 802 b based on a comparison of the human-providedvalue of 9 to the integer scale of 0-10 on which it is based forconversion to the same one hundred point scale and generate a secondnormalized model score 804 b of 90.

With respect to the third feeding appetite forecast, the systemsnormalize the third appetite descriptor 802 c of the green color basedon a comparison to a color-coded descriptor of hunger in a red, yellow,and green coloring scheme in which green is indicative of increasedhunger levels to generate a third normalized model score 804 c of 70 inthe one hundred point common scale. With respect to the fourth feedingappetite forecast, the systems normalize the fourth appetite descriptor802 d of the percentage 80% based on a comparison relative to anexpected base behavior (i.e., expected portion of total biomass that isexpected to be located within, for illustrative purposes, an upper ⅓ ofthe volumetric enclosure within which the fish are located is indicativeof increased hunger levels). In this illustrative example, 80% ofbiomass being above the threshold level generates a fourth normalizedmodel score 804 d of 55 in the one hundred point common scale. Lastly,with respect to the fifth feeding appetite forecast, the systemsnormalize the fifth appetite descriptor 802 e of 0.4 PPFPM based on acomparison to historical feed rates to generate a fifth normalized modelscore 804 e of 50 in the one hundred point common scale.

It should be recognized that the above-mentioned bases for normalizationof disparate appetite descriptors having different individual underlyingscales (and also the one hundred point normalization scale) are providedonly for simplified illustrative purposes only. In various embodiments,the systems described herein may utilize any appropriate basis forconversion of the different appetite descriptors 802 a-802 e to a commonnormalization scale as will be understood by those skilled in the art.

The systems will adaptively weight the first through fifth feedingappetite models and their associated appetite descriptors/normalizedscore values in a manner similar to that previously discussed in moredetail relative to FIGS. 1-7. For ease of illustration, the weightinghas been shown in FIG. 8 to be equal weighted such that the systemsassign a first weighting factor w₁ of 0.2 to the first feeding appetiteforecast model, a second weighting factor w₂ of 0.2 to the secondfeeding appetite forecast model, a third weighting factor w₃ of 0.2 tothe third feeding appetite forecast model, a fourth weighting factor w₄of 0.2 to the fourth feeding appetite forecast model, and a fifthweighting factor w₅ of 0.2 to the fifth feeding appetite forecast model.Subsequently, the systems applies the assigned weighting factors w₁-w₅to the first through fifth normalized model scores 804 a-804 e,respectively to generate a first weighted model score 806 a of 15, asecond weighted model score 806 b of 18, a third weighted model score806 c of 14, a fourth weighted model score 806 d of 11, and a fifthweighted model score 806 e of 10. Further, the system combines thesefive weighted model scores 806 a-806 e to generate a weighted,aggregated appetite score 808 of 68 and thereby integrates data frommulti-sensor systems to provide an appetite forecast score.

In various embodiments, the aggregated appetite score 808 may bedisplayed in a graphical user interface for presentation to a user. Forexample, a value of 68 (within a 100 point scale) for the aggregatedappetite score 808 provides context regarding expected appetite levels,and the user may take action accordingly. In other embodiments, thesystems may optionally generate a feeding instruction signal 810 (asrepresented by the dotted line box) that instructs a user and/orautomated feeding system regarding specific actions to be taken inaccordance to the feeding appetite prediction (e.g., as quantified bythe aggregated appetite score 808). Similar to the first through fifthfeeding appetite forecasts discussed here with respect to FIG. 8, itwill be appreciated that the feeding instruction signal 810 is notlimited to any particular format and in various embodiments may, in amanner similar to a reversing of the previous normalization operations,be converted to any appropriate format. Such formats for the feedinginstruction signal 810 include, by way of non-limiting example, a stopsignal, a color-coded user interface display, a specific feed rate thatshould be administered, a total feed volume that should be administered,and the like.

Although primarily discussed here in the context of aquaculture as itrelates to the feeding of fish, those skilled in the art will recognizethat the techniques described herein may be applied to any aquatic,aquaculture species such as shellfish, crustaceans, bivalves, and thelike without departing from the scope of this disclosure. Further, thoseskilled in the art will recognize that the techniques described hereinmay also be applied to adaptively weighting feeding models and providingaggregated appetite forecasting for any husbandry animal that is rearedin an environment in which multiple different sensor systems areapplied, and for which the sensor systems will vary in accuracy ofappetite prediction depending on environmental conditions, differingavailability of data over time, differing data granularities, and thelike.

Additionally, although primarily illustrated and discussed here in thecontext of fish being positioned in an open water environment (whichwill also include an enclosure of some kind to prevent escape of fishinto the open ocean), those skilled in the art will recognize that thetechniques described herein may similarly be applied to any type ofaquatic farming environment. For example, such aquatic farmingenvironments may include, by way of non-limiting example, lakes, ponds,open seas, recirculation aquaculture systems (RAS) to provide for closedsystems, raceways, indoor tanks, outdoor tanks, and the like.

Accordingly, as discussed herein, FIGS. 1-8 describe techniques thatimprove the precision and accuracy of feeding appetite forecasting anddecreasing the uncertainties associated with conventional appetiteprediction systems by adaptively weighting different feeding appetitepredictions from different feeding appetite forecast models andcombining them into an aggregated appetite score that is more accuratethan would be individually provided by each feeding appetite forecastmodel by itself. Further, the techniques described here provide anefficient manner for farmers to integrate the ever increasing suite ofavailable sensor technologies in the future with any sensors currentlyutilized at their farming sites to improve feeding and the results ofaquaculture operations.

In some embodiments, certain aspects of the techniques described abovemay implemented by one or more processors of a processing systemexecuting software. The software includes one or more sets of executableinstructions stored or otherwise tangibly embodied on a non-transitorycomputer readable storage medium. A computer readable storage medium mayinclude any non-transitory storage medium, or combination ofnon-transitory storage media, accessible by a computer system during useto provide instructions and/or data to the computer system. Such storagemedia can include, but is not limited to, optical media (e.g., compactdisc (CD), digital versatile disc (DVD), Blu-Ray disc), magnetic media(e.g., floppy disc , magnetic tape, or magnetic hard drive), volatilememory (e.g., random access memory (RAM) or cache), non-volatile memory(e.g., read-only memory (ROM) or Flash memory), ormicroelectromechanical systems (MEMS)-based storage media. The computerreadable storage medium may be embedded in the computing system (e.g.,system RAM or ROM), fixedly attached to the computing system (e.g., amagnetic hard drive), removably attached to the computing system (e.g.,an optical disc or Universal Serial Bus (USB)-based Flash memory), orcoupled to the computer system via a wired or wireless network (e.g.,network accessible storage (NAS)).

The software can include the instructions and certain data that, whenexecuted by the one or more processors, manipulate the one or moreprocessors to perform one or more aspects of the techniques describedabove. The non-transitory computer readable storage medium can include,for example, a magnetic or optical disk storage device, solid statestorage devices such as Flash memory, a cache, random access memory(RAM) or other non-volatile memory device or devices, and the like. Theexecutable instructions stored on the non-transitory computer readablestorage medium may be in source code, assembly language code, objectcode, or other instruction format that is interpreted or otherwiseexecutable by one or more processors.

Note that not all of the activities or elements described above in thegeneral description are required, that a portion of a specific activityor device may not be required, and that one or more further activitiesmay be performed, or elements included, in addition to those described.Still further, the order in which activities are listed are notnecessarily the order in which they are performed. Also, the conceptshave been described with reference to specific embodiments. However, oneof ordinary skill in the art appreciates that various modifications andchanges can be made without departing from the scope of the presentdisclosure as set forth in the claims below. Accordingly, thespecification and figures are to be regarded in an illustrative ratherthan a restrictive sense, and all such modifications are intended to beincluded within the scope of the present disclosure.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any feature(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature of any or all the claims. Moreover, the particular embodimentsdisclosed above are illustrative only, as the disclosed subject mattermay be modified and practiced in different but equivalent mannersapparent to those skilled in the art having the benefit of the teachingsherein. No limitations are intended to the details of construction ordesign herein shown, other than as described in the claims below. It istherefore evident that the particular embodiments disclosed above may bealtered or modified and all such variations are considered within thescope of the disclosed subject matter. Accordingly, the protectionsought herein is as set forth in the claims below.

1-20. (canceled)
 21. A method, comprising: receiving, at a processingsystem, a first forecast parameter data set associated with a firstfeeding parameter, wherein the first feeding parameter includes at leastone of image data and hydroacoustic data corresponding to behavior ofunderwater objects; receiving, at the processing system, a secondforecast parameter data set associated with a second feeding parameterdifferent from the first feeding parameter; adaptively weighting, basedat least in part a data granularity difference between the hydroacousticdata and the second forecast parameter data set, the first feedingparameter with a first weighting factor relative to a second weightingfactor for the second feeding parameter; and outputting, using acombination of the first forecast parameter data set and the secondforecast parameter data set based on the first weight factor and thesecond weight factor, an aggregated feeding appetite forecast.
 22. Themethod of claim 21, wherein receiving the first forecast parameter dataset further comprises: receiving hydroacoustic data measuring fishpositions within an aquatic enclosure; and providing the hydroacousticdata to a first feeding appetite forecast model for generating a firstfeeding appetite forecast.
 23. The method of claim 22, wherein receivinghydroacoustic data further comprises: receiving hydroacoustic datameasuring one or more of fish swim behavior, fish populationdistribution, and feeding-generated sounds within the aquatic enclosure.24. The method of claim 21, wherein adaptively weighting the firstfeeding parameter with the first weighting factor relative to the secondweighting factor for the second feeding parameter further comprises:receiving a measured reference data related to the first feedingparameter and the second feeding parameter; assigning the first weightfactor to the first feeding parameter based on a comparison of the firstforecast parameter data set with the measured reference data; andassigning the second weight factor to the second feeding parameter basedon a comparison of the second feeding parameter data set with themeasured reference data.
 25. The method of claim 21, wherein receivingthe second forecast parameter data set further comprises: receivingimage data measuring underwater object positions within an aquaticenclosure; and providing the image data to a second feeding appetiteforecast model for generating a second feeding appetite forecast. 26.The method of claim 25, wherein receiving image data further comprises:receiving image data measuring one or more of feed pellet positions,animal positions, and animal behavior within the aquatic enclosure. 27.The method of claim 21, further comprising: generating, based at leastin part on the aggregated feeding appetite forecast, a feedingrecommendation related to administration of feed.
 28. The method ofclaim 27, further comprising: providing a feeding instruction signalbased on one or more of the feeding recommendation and the aggregatedfeeding appetite forecast for modifying operations of a feed controlsystem.
 29. A non-transitory computer readable medium embodying a set ofexecutable instructions, the set of executable instructions tomanipulate at least one processor to: receive, at a processing system, afirst forecast parameter data set associated with a first feedingparameter, wherein the first feeding parameter includes at least one ofimage data and hydroacoustic data corresponding to behavior ofunderwater objects; receive, at the processing system, a second forecastparameter data set associated with a second feeding parameter differentfrom the first feeding parameter; adaptively weight, based at least inpart a data granularity difference between the hydroacoustic data andthe second forecast parameter data set, the first feeding parameter witha first weighting factor relative to a second weighting factor for thesecond feeding parameter; and output, using a combination of the firstforecast parameter data set and the second forecast parameter data setbased on the first weight factor and the second weight factor, anaggregated feeding appetite forecast.
 30. The non-transitory computerreadable medium of claim 29, further embodying executable instructionsto manipulate at least one processor to: receive hydroacoustic datameasuring fish positions within an aquatic enclosure; and provide thehydroacoustic data to a first feeding appetite forecast model forgenerating a first feeding appetite forecast.
 31. The non-transitorycomputer readable medium of claim 30, further embodying executableinstructions to manipulate at least one processor to: receivehydroacoustic data measuring one or more of fish swim behavior, fishpopulation distribution, and feeding-generated sounds within the aquaticenclosure.
 32. The non-transitory computer readable medium of claim 29,further embodying executable instructions to manipulate at least oneprocessor to: receive a measured reference data related to the firstfeeding parameter and the second feeding parameter; assign the firstweight factor to the first feeding parameter based on a comparison ofthe first forecast parameter data set with the measured reference data;and assign the second weight factor to the second feeding parameterbased on a comparison of the second feeding parameter data set with themeasured reference data.
 33. The non-transitory computer readable mediumof claim 29, further embodying executable instructions to manipulate atleast one processor to: receive image data measuring underwater objectpositions within an aquatic enclosure; and provide the image data to asecond feeding appetite forecast model for generating a second feedingappetite forecast.
 34. The non-transitory computer readable medium ofclaim 29, further embodying executable instructions to manipulate atleast one processor to: generate, based at least in part on theaggregated feeding appetite forecast, a feeding recommendation relatedto administration of feed.
 35. The non-transitory computer readablemedium of claim 34, further embodying executable instructions tomanipulate at least one processor to: provide a feeding instructionsignal based on one or more of the feeding recommendation and theaggregated feeding appetite forecast for modifying operations of a feedcontrol system.
 36. A system, comprising: a first set of one or moresensors configured to generate a first feeding parameter data setassociated with a first feeding parameter, wherein the first feedingparameter includes at least one of image data and hydroacoustic datacorresponding to behavior of underwater objects; a second set of one ormore sensors configured to generate a second feeding parameter data setassociated with a second feeding parameter; a digital storage medium,encoding instructions executable by a computing device; a processor,communicably coupled to the digital storage medium, configured toexecute the instructions, wherein the instructions are configured to:adaptively weight, based at least in part a data granularity differencebetween the hydroacoustic data and the second forecast parameter dataset, the first feeding parameter with a first weighting factor relativeto a second weighting factor for the second feeding parameter; andoutput, using a combination of the first forecast parameter data set andthe second forecast parameter data set based on the first weight factorand the second weight factor, an aggregated feeding appetite forecast.37. The system of claim 36, wherein the processor is further configuredto: receive hydroacoustic data measuring fish positions within anaquatic enclosure; and provide the hydroacoustic data to a first feedingappetite forecast model for generating a first feeding appetiteforecast.
 38. The system of claim 36, wherein the processor is furtherconfigured to: receive a measured reference data related to the firstfeeding parameter and the second feeding parameter; assign the firstweight factor to the first feeding parameter based on a comparison ofthe first forecast parameter data set with the measured reference data;and assign the second weight factor to the second feeding parameterbased on a comparison of the second feeding parameter data set with themeasured reference data.
 39. The system of claim 36, wherein theprocessor is further configured to: receive image data measuringunderwater object positions within an aquatic enclosure; and provide theimage data to a second feeding appetite forecast model for generating asecond feeding appetite forecast.
 40. The system of claim 36, whereinthe processor is further configured to: generate, based at least in parton the aggregated feeding appetite forecast, a feeding recommendationrelated to administration of feed.